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llama : move end-user examples to tools directory (#13249)

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* llama : move end-user examples to tools directory

---------

Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
This commit is contained in:
Diego Devesa 2025-05-02 20:27:13 +02:00 • committed by GitHub
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39
tools/CMakeLists.txt Normal file
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# dependencies
find_package(Threads REQUIRED)
# third-party
# ...
# flags
llama_add_compile_flags()
# tools
if (EMSCRIPTEN)
else()
add_subdirectory(batched-bench)
add_subdirectory(gguf-split)
add_subdirectory(imatrix)
add_subdirectory(llama-bench)
add_subdirectory(main)
add_subdirectory(perplexity)
add_subdirectory(quantize)
if (LLAMA_BUILD_SERVER)
add_subdirectory(server)
endif()
add_subdirectory(run)
add_subdirectory(tokenize)
add_subdirectory(tts)
if (NOT GGML_BACKEND_DL)
# these examples use the backends directly and cannot be built with dynamic loading
add_subdirectory(cvector-generator)
add_subdirectory(export-lora)
add_subdirectory(llava)
if (GGML_RPC)
add_subdirectory(rpc)
endif()
endif()
endif()

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set(TARGET llama-batched-bench)
add_executable(${TARGET} batched-bench.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# llama.cpp/example/batched-bench
Benchmark the batched decoding performance of `llama.cpp`
## Usage
There are 2 modes of operation:
- `prompt not shared` - each batch has a separate prompt of size `PP` (i.e. `N_KV = B*(PP + TG)`)
- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
```bash
./llama-batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]
# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
./llama-batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99
# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps
# custom set of batches
./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32
```
## Sample results
- `PP` - prompt tokens per batch
- `TG` - generated tokens per batch
- `B` - number of batches
- `N_KV` - required KV cache size
- `T_PP` - prompt processing time (i.e. time to first token)
- `S_PP` - prompt processing speed (`(B*PP)/T_PP` or `PP/T_PP`)
- `T_TG` - time to generate all batches
- `S_TG` - text generation speed (`(B*TG)/T_TG`)
- `T` - total time
- `S` - total speed (i.e. all tokens / total time)
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
| 128 | 128 | 1 | 256 | 0.108 | 1186.64 | 3.079 | 41.57 | 3.187 | 80.32 |
| 128 | 128 | 2 | 512 | 0.198 | 1295.19 | 5.029 | 50.90 | 5.227 | 97.95 |
| 128 | 128 | 4 | 1024 | 0.373 | 1373.96 | 6.878 | 74.44 | 7.251 | 141.23 |
| 128 | 128 | 8 | 2048 | 0.751 | 1363.27 | 7.344 | 139.43 | 8.095 | 252.99 |
| 128 | 128 | 16 | 4096 | 1.570 | 1304.68 | 8.455 | 242.23 | 10.024 | 408.60 |
| 128 | 128 | 32 | 8192 | 3.408 | 1201.73 | 8.801 | 465.40 | 12.209 | 670.96 |
| 128 | 256 | 1 | 384 | 0.107 | 1196.70 | 6.329 | 40.45 | 6.436 | 59.67 |
| 128 | 256 | 2 | 768 | 0.194 | 1317.45 | 10.239 | 50.00 | 10.433 | 73.61 |
| 128 | 256 | 4 | 1536 | 0.366 | 1399.03 | 13.960 | 73.35 | 14.326 | 107.22 |
| 128 | 256 | 8 | 3072 | 0.751 | 1363.92 | 15.110 | 135.54 | 15.861 | 193.69 |
| 128 | 256 | 16 | 6144 | 1.569 | 1304.93 | 18.073 | 226.64 | 19.642 | 312.80 |
| 128 | 256 | 32 | 12288 | 3.409 | 1201.35 | 19.223 | 426.15 | 22.633 | 542.93 |
### JSONL output
Pass `--output-format jsonl` to output JSONL instead of Markdown, á la
```json lines
{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 1, "n_kv": 256, "t_pp": 0.233810, "speed_pp": 547.453064, "t_tg": 3.503684, "speed_tg": 36.532974, "t": 3.737494, "speed": 68.495094}
{"n_kv_max": 2048, "n_batch": 2048, "n_ubatch": 512, "flash_attn": 0, "is_pp_shared": 0, "n_gpu_layers": 99, "n_threads": 8, "n_threads_batch": 8, "pp": 128, "tg": 128, "pl": 2, "n_kv": 512, "t_pp": 0.422602, "speed_pp": 605.770935, "t_tg": 11.106112, "speed_tg": 23.050371, "t": 11.528713, "speed": 44.410854}
```

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#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include <algorithm>
#include <cstdio>
#include <string>
#include <vector>
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]);
LOG("\n");
}
int main(int argc, char ** argv) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_BENCH, print_usage)) {
return 1;
}
common_init();
int is_pp_shared = params.is_pp_shared;
std::vector<int> n_pp = params.n_pp;
std::vector<int> n_tg = params.n_tg;
std::vector<int> n_pl = params.n_pl;
// init LLM
llama_backend_init();
llama_numa_init(params.numa);
// initialize the model
llama_model_params model_params = common_model_params_to_llama(params);
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params);
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
llama_context_params ctx_params = common_context_params_to_llama(params);
// ensure enough sequences are available
ctx_params.n_seq_max = n_pl.empty() ? 1 : *std::max_element(n_pl.begin(), n_pl.end());
llama_context * ctx = llama_init_from_model(model, ctx_params);
if (ctx == NULL) {
fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
return 1;
}
const int32_t n_kv_max = llama_n_ctx(ctx);
llama_batch batch = llama_batch_init(n_kv_max, 0, 1);
// decode in batches of ctx_params.n_batch tokens
auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch) {
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
};
const int ret = llama_decode(ctx, batch_view);
if (ret != 0) {
LOG_ERR("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
return false;
}
llama_synchronize(ctx);
}
return true;
};
// warm up
{
for (int i = 0; i < 16; ++i) {
common_batch_add(batch, 0, i, { 0 }, false);
}
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
}
if (!params.batched_bench_output_jsonl) {
LOG("\n");
LOG("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG("\n");
LOG("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");
LOG("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
}
for ( int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) {
for ( int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) {
for (int i_pl = 0; i_pl < (int) n_pl.size(); ++i_pl) {
const int pp = n_pp[i_pp];
const int tg = n_tg[i_tg];
const int pl = n_pl[i_pl];
const int n_ctx_req = is_pp_shared ? pp + pl*tg : pl*(pp + tg);
if (n_ctx_req > n_kv_max) {
continue;
}
common_batch_clear(batch);
for (int i = 0; i < pp; ++i) {
for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
common_batch_add(batch, 0, i, { j }, false);
}
}
batch.logits[batch.n_tokens - 1] = true;
const auto t_pp_start = ggml_time_us();
llama_kv_self_clear(ctx);
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
if (is_pp_shared) {
for (int32_t i = 1; i < pl; ++i) {
llama_kv_self_seq_cp(ctx, 0, i, -1, -1);
}
}
const auto t_pp_end = ggml_time_us();
const auto t_tg_start = ggml_time_us();
for (int i = 0; i < tg; ++i) {
common_batch_clear(batch);
for (int j = 0; j < pl; ++j) {
common_batch_add(batch, 0, pp + i, { j }, true);
}
if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
LOG_ERR("%s: llama_decode() failed\n", __func__);
return 1;
}
}
const auto t_tg_end = ggml_time_us();
const int32_t n_kv = n_ctx_req;
const float t_pp = (t_pp_end - t_pp_start) / 1000000.0f;
const float t_tg = (t_tg_end - t_tg_start) / 1000000.0f;
const float t = t_pp + t_tg;
const float speed_pp = is_pp_shared ? pp / t_pp : pl*pp / t_pp;
const float speed_tg = pl*tg / t_tg;
const float speed = n_kv / t;
if(params.batched_bench_output_jsonl) {
LOG(
"{\"n_kv_max\": %d, \"n_batch\": %d, \"n_ubatch\": %d, \"flash_attn\": %d, \"is_pp_shared\": %d, \"n_gpu_layers\": %d, \"n_threads\": %u, \"n_threads_batch\": %u, "
"\"pp\": %d, \"tg\": %d, \"pl\": %d, \"n_kv\": %d, \"t_pp\": %f, \"speed_pp\": %f, \"t_tg\": %f, \"speed_tg\": %f, \"t\": %f, \"speed\": %f}\n",
n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch,
pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed
);
} else {
LOG("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);
}
}
}
}
LOG("\n");
llama_perf_context_print(ctx);
llama_batch_free(batch);
llama_free(ctx);
llama_model_free(model);
llama_backend_free();
LOG("\n\n");
return 0;
}

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set(TARGET llama-cvector-generator)
add_executable(${TARGET} cvector-generator.cpp pca.hpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# cvector-generator
This example demonstrates how to generate a control vector using gguf models.
Related PRs:
- [Add support for control vectors](https://github.com/ggml-org/llama.cpp/pull/5970)
- (Issue) [Generate control vector using llama.cpp](https://github.com/ggml-org/llama.cpp/issues/6880)
- [Add cvector-generator example](https://github.com/ggml-org/llama.cpp/pull/7514)
## Examples
```sh
# CPU only
./cvector-generator -m ./llama-3.Q4_K_M.gguf
# With GPU
./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99
# With advanced options
./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100
# Using mean value instead of PCA
./cvector-generator -m ./llama-3.Q4_K_M.gguf --method mean
# To see help message
./cvector-generator -h
# Then, have a look at "cvector" section
```
## Tips and tricks
If you have multiple lines per prompt, you can escape the newline character (change it to `\n`). For example:
```
<|im_start|>system\nAct like a person who is extremely happy.<|im_end|>
<|im_start|>system\nYou are in a very good mood today<|im_end|>
```
Example to use output file with `llama-cli`:
(Tips: The control vector works better when apply to layers higher than 10)
```sh
./llama-cli -m ./llama-3.Q4_K_M.gguf -p "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSing a song<|im_end|><|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" --special --control-vector-scaled ./control_vector.gguf 0.8 --control-vector-layer-range 10 31
```

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tools/cvector-generator/completions.txt Normal file
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That game
I can see
Hmm, this
I can relate to
Who is
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Hey, did anyone
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It's always
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I understand that
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The document
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I can see that
Wow,
Today is a
Hey friend
Sometimes friends
Oh, this old
The weather outside
This place is sur
I appreciate your input
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I'm disappoint
To my
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This piece of art
Eww
This park is
This is incredible
Oh no, someone
Exc
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I warned
Hey, I understand
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What the he
Hey
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It
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This is the perfect
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It's so
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Hello everyone
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Excuse
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I am deeply sad
I war
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What happened
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The artist'
What?!
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I am disappoint
It seems like
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Bless you!\"
Thank you for
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Can you please
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Is everything
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Whoever left these
Well, that'
I feel
Hey, do you
It's sad
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Hey, that'
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Thank you,
Hello little one,
I apolog
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Who is this and
Whoever left
Hi there! W
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Is everything okay?
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Who is it?\"
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I would love to
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Good morning
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That poem really reson
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I miss
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I understand that things
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This post
Are
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I can only imagine
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Hello,
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This postcard
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That movie
I remember wondering about
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Hello, this
That movie really touched
If you have something
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That melody really
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I apologize
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It's such
It's clear
Hey, I was
Hey friend,
I can only
The weather outside is
Eww, this
I miss you
Wow
Aww,
Hi, is there
This artwork
Okay,
Oh well,
This
I'
Say
Hey there little gu
Hmm,
Whoa, who
I am thr
Oh man
Okay, stay calm
I'm happy
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Oh man,
I'm sorry
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What?! That
This piece
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That's so
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What happened? Where
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508
tools/cvector-generator/cvector-generator.cpp Normal file
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#include "ggml.h"
#include "gguf.h"
#include "arg.h"
#include "common.h"
#include "llama.h"
#include "pca.hpp"
#include "mean.hpp"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <algorithm>
#include <climits>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <iostream>
#include <string>
#include <tuple>
#include <vector>
//////////////////////////////////////////////////
// utils
template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += common_token_to_piece(ctx, *begin);
}
return ret;
}
static void print_usage(int, char ** argv) {
printf("\nexample usage:\n");
printf("\n CPU only: %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]);
printf("\n with GPU: %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]);
printf("\n advanced: %s -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100\n", argv[0]);
printf("\n using mean: %s -m ./llama-3.Q4_K_M.gguf --method mean\n", argv[0]);
printf("\n");
}
//////////////////////////////////////////////////
// cb_eval is reused for each pair of positive - negative prompt
struct callback_data {
ggml_context * ctx_ggml = nullptr; // holds v_pos, v_neg, v_diff_filtered
int n_layers = 0;
int n_tokens = 0;
bool is_eval_pos = true;
// each element of the vector correspond to one layer
std::vector<struct ggml_tensor *> v_pos; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_neg; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_diff_filtered; // vector of matrices of size [n_embd, n_nonzero_rows]. NOTE: n_nonzero_rows maybe different for each layer
// save a tensor into either v_pos or v_neg (decided by is_eval_pos)
void save_tensor_for_layer(struct ggml_tensor * t) {
GGML_ASSERT(t->type == GGML_TYPE_F32);
if (ctx_ggml == nullptr) {
// alloc a new ctx_ggml if needed
struct ggml_init_params params_ggml = {
/*.mem_size =*/ ggml_tensor_overhead() * n_layers * 3u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx_ggml = ggml_init(params_ggml);
}
// copy tensor data
auto n_bytes = ggml_nbytes(t);
struct ggml_tensor * t_layer = ggml_new_tensor_2d(ctx_ggml, t->type, t->ne[0], t->ne[1]);
t_layer->data = malloc(n_bytes); // TODO @ngxson : get rid of this malloc somehow
ggml_backend_tensor_get(t, t_layer->data, 0, n_bytes);
ggml_set_name(t_layer, ggml_get_name(t));
//print_debug_tensor(t_layer);
if (is_eval_pos) {
v_pos.push_back(t_layer);
} else {
v_neg.push_back(t_layer);
}
}
// calculate diff (v_pos - v_neg) and place the result back to v_pos
// all zero rows in the diff tensor will also be removed
// NOTE: final layer is ignored. we only have (n_layers - 1) to process
std::vector<struct ggml_tensor *> calc_diff() {
for (float il = 0; il < v_pos.size(); il++) {
float * a = (float *) v_pos[il]->data;
float * b = (float *) v_neg[il]->data;
size_t n_elem = ggml_nelements(v_pos[il]);
for (size_t j = 0; j < n_elem; j++) {
a[j] -= b[j];
}
//print_debug_tensor(v_pos[i]);
auto diff_filtered = filter_nonzero_rows(v_pos[il]);
v_diff_filtered.push_back(diff_filtered);
}
return v_diff_filtered; // for convinient, we return the result std::vector
}
// delete zero rows from a given 2D tensor
struct ggml_tensor * filter_nonzero_rows(struct ggml_tensor * a) {
//printf("filter_nonzero_rows\n");
auto is_row_all_zeros = [](struct ggml_tensor * t, int row, float eps) -> bool {
// check if given row containing all zero elements
int n_cols = t->ne[0]; // hint: should be equal to n_embd
for (int col = 0; col < n_cols; ++col) {
if (ggml_get_f32_nd(t, col, row, 0, 0) > eps) {
return false;
}
}
return true;
};
std::vector<int> rows_to_copy; // the idx of non-zero cols (to be copied to row of diff_filtered)
for (int i_row = 0; i_row < a->ne[1]; i_row++) {
if (!is_row_all_zeros(a, i_row, 1e-6)) {
rows_to_copy.push_back(i_row);
}
}
// get "n_nonzero_rows" for the output "diff_filtered"
int n_nonzero_rows = rows_to_copy.size();
//printf("n_nonzero_rows: %d\n", n_nonzero_rows);
int n_embd = a->ne[0];
GGML_ASSERT(n_nonzero_rows > 0);
// diff_filtered: [n_embd, n_nonzero_rows]
struct ggml_tensor * diff_filtered = ggml_new_tensor_2d(
ctx_ggml, GGML_TYPE_F32, n_embd, n_nonzero_rows);
ggml_format_name(diff_filtered, "diff_filtered_%s", a->name);
diff_filtered->data = malloc(ggml_nbytes(diff_filtered));
// copy non-zero rows
for (int dest_row = 0; dest_row < n_nonzero_rows; dest_row++) {
int src_row = rows_to_copy[dest_row];
for (int i = 0; i < n_embd; i++) {
float src_elem = ggml_get_f32_nd(a, i, src_row, 0, 0);
ggml_set_f32_nd(diff_filtered, i, dest_row, 0, 0, src_elem);
}
}
//print_debug_tensor(diff_filtered);
return diff_filtered;
}
// we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors
void reset() {
for (auto ptr : v_pos) free(ptr->data);
for (auto ptr : v_neg) free(ptr->data);
for (auto ptr : v_diff_filtered) free(ptr->data);
v_pos.clear();
v_neg.clear();
v_diff_filtered.clear();
if (ctx_ggml) {
ggml_free(ctx_ggml);
}
ctx_ggml = nullptr;
}
};
/**
* process_ctx is used to store the ggml context for pre-post processing the diff vectors
* in short, input => v_diff and output => v_final
*/
struct train_context {
ggml_context * ctx_ggml;
int n_embd;
int n_layers;
/* pair of prompts to be used for generating final vector */
std::vector<std::string> positive_entries;
std::vector<std::string> negative_entries;
// each element of the vector correspond to one layer
// NOTE: the last layer is discard. therefore, we will have (n_layers - 1) elements here
// NOTE (2): v_diff is transposed from v_diff_tmp
std::vector<struct ggml_tensor *> v_diff; // vector of matrices of size [m, n_embd] where m ~ n_tokens * n_completions (v_diff contains no zero-rows)
std::vector<struct ggml_tensor *> v_final; // vector of vectors of size [n_embd] to be written to file
// to easily re-alloc when concat v_diff, we temporary store v_diff in a vector instead of a tensor
// v_diff_tmp will get converted unto v_diff later on
std::vector<std::vector<uint8_t>> v_diff_tmp;
train_context(int n_embd_, int n_layers_) {
n_embd = n_embd_;
n_layers = n_layers_;
struct ggml_init_params params_ggml = {
/*.mem_size =*/ ggml_tensor_overhead() * (n_layers - 1) * 2u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx_ggml = ggml_init(params_ggml);
for (int il = 0; il < n_layers - 1; il++) {
std::vector<uint8_t> empty;
v_diff_tmp.push_back(empty);
auto t = ggml_new_tensor_1d(ctx_ggml, GGML_TYPE_F32, n_embd);
t->data = malloc(ggml_nbytes(t)); // TODO: get rid of malloc if possible
v_final.push_back(t);
}
}
// add new rows into existing tensor in v_diff_tmp
void concat_diff_tmp(const std::vector<struct ggml_tensor *> & diff_filtered) {
GGML_ASSERT((int) diff_filtered.size() == n_layers - 1);
for (int il = 0; il < n_layers - 1; il++) {
auto t = diff_filtered[il];
auto & diff_tmp = v_diff_tmp[il];
size_t curr_size = diff_tmp.size();
diff_tmp.resize(curr_size + ggml_nbytes(t));
memcpy(diff_tmp.data() + curr_size, t->data, ggml_nbytes(t));
}
}
// build the v_diff tensors from v_diff_tmp (v_diff need to be transposed)
// TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method
void build_v_diff(bool transpose) {
printf("build_v_diff\n");
for (int il = 0; il < n_layers - 1; il++) {
auto & diff_tmp = v_diff_tmp[il];
int n_elem = diff_tmp.size() / sizeof(float);
GGML_ASSERT(n_elem % n_embd == 0);
int n_rows = n_elem / n_embd;
struct ggml_tensor * diff = transpose
? ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd)
: ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_embd, n_rows);
ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str());
diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible
if (transpose) {
// copy data & transpose
float * arr = (float *) diff_tmp.data();
for (int ir = 0; ir < n_rows; ++ir) {
for (int ic = 0; ic < n_embd; ++ic) {
float f = arr[ir*n_embd + ic];
ggml_set_f32_nd(diff, ir, ic, 0, 0, f);
}
}
} else {
// only copy
memcpy(diff->data, diff_tmp.data(), ggml_nbytes(diff));
}
v_diff.push_back(diff);
print_debug_tensor(diff);
// free memory of diff_tmp
diff_tmp.resize(0);
}
}
~train_context() {
for (auto ptr : v_final) free(ptr->data);
for (auto ptr : v_diff) free(ptr->data);
// no need to free v_diff_tmp, since we didn't use malloc
ggml_free(ctx_ggml);
}
};
struct tokenized_prompt {
std::vector<llama_token> tokens_pos;
std::vector<llama_token> tokens_neg;
size_t max_seq_len;
tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
tokens_pos = common_tokenize(ctx, pos, add_bos, true);
tokens_neg = common_tokenize(ctx, neg, add_bos, true);
max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
padding_seq(ctx, tokens_pos, max_seq_len);
padding_seq(ctx, tokens_neg, max_seq_len);
}
void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) {
// TODO: customize padding token
std::vector<llama_token> pad_tokens = common_tokenize(ctx, " ", false);
llama_token pad_tok = pad_tokens.back();
while (tokens.size() < len) {
tokens.push_back(pad_tok);
}
}
};
//////////////////////////////////////////////////
template <typename T>
static std::string to_string(const T & val) {
std::stringstream ss;
ss << val;
return ss.str();
}
static std::vector<std::string> ctrlvec_load_prompt_file(std::string path, bool skip_empty_lines) {
std::vector<std::string> output;
std::ifstream file(path);
if (!file.is_open()) {
fprintf(stderr, "error: unable to open file: %s\n", path.c_str());
exit(1);
}
std::string line;
while (std::getline(file, line)) {
bool is_skip = skip_empty_lines && line.empty();
if (!is_skip) {
string_process_escapes(line);
output.push_back(line);
}
}
file.close();
return output;
}
//////////////////////////////////////////////////
static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
auto * cb_data = (callback_data *) user_data;
static const char * l_out_name = "l_out";
const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0;
if (ask) {
return is_l_out;
}
if (!is_l_out || t->ne[1] != cb_data->n_tokens) {
return true;
}
// save the tensor to current context
cb_data->save_tensor_for_layer(t);
return true;
}
static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
llama_kv_self_clear(ctx);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
return true;
}
static void export_gguf(const std::vector<struct ggml_tensor *> & v_ctrl, const std::string fname, const std::string model_hint) {
struct gguf_context * ctx = gguf_init_empty();
const std::string arch = "controlvector";
gguf_set_val_str(ctx, "general.architecture", arch.c_str());
gguf_set_val_str(ctx, (arch + ".model_hint").c_str(), model_hint.c_str());
gguf_set_val_i32(ctx, (arch + ".layer_count").c_str(), v_ctrl.size());
for (size_t i = 0; i < v_ctrl.size(); ++i) {
gguf_add_tensor(ctx, v_ctrl[i]);
print_debug_tensor(v_ctrl[i]);
printf("Added tensor: %s\n", v_ctrl[i]->name);
}
printf("%s: writing file...\n", __func__);
gguf_write_to_file(ctx, fname.c_str(), false);
printf("%s: wrote file '%s'\n", __func__, fname.c_str());
gguf_free(ctx);
}
/**
* Load prompt files and completion file.
* Then format each pair of prompt + completion to make an entry.
*/
static int prepare_entries(common_params & params, train_context & ctx_train) {
// load prompts
std::vector<std::string> positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true);
std::vector<std::string> negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true);
if (positive_prompts.size() != negative_prompts.size()) {
fprintf(stderr, "number of positive and negative prompts must be equal\n");
return 1;
}
if (positive_prompts.empty()) {
fprintf(stderr, "must provide at least one prompt pair\n");
return 1;
}
ctx_train.positive_entries = positive_prompts;
ctx_train.negative_entries = negative_prompts;
return 0;
}
int main(int argc, char ** argv) {
common_params params;
params.out_file = "control_vector.gguf";
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
return 1;
}
if (params.n_pca_iterations % params.n_pca_batch != 0) {
fprintf(stderr, "PCA iterations must by multiply of PCA batch size\n");
return 1;
}
callback_data cb_data;
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
params.cb_eval = cb_eval;
params.cb_eval_user_data = &cb_data;
params.warmup = false;
print_build_info();
llama_backend_init();
llama_numa_init(params.numa);
// load the model to get hparams
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
// int n_ctx = llama_n_ctx(ctx);
int n_layers = llama_model_n_layer(model);
int n_embd = llama_model_n_embd(model);
// get model hint param (a.k.a model arch name)
char model_hint[128];
llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
// init train_context
train_context ctx_train(n_embd, n_layers);
// load and prepare entries for training
prepare_entries(params, ctx_train);
// we have to pretokenize everything because otherwise we don't know how much overhead to allocate ctx_diffs_wrapped
std::vector<tokenized_prompt> tokenized_prompts;
size_t n_total_tokens = 0;
for (size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
tokenized_prompt t(ctx, ctx_train.positive_entries[i], ctx_train.negative_entries[i]);
n_total_tokens += 2 * t.max_seq_len;
tokenized_prompts.push_back(std::move(t));
}
std::cout << "n_total_tokens: " << n_total_tokens << std::endl;
for(size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
bool success = false;
tokenized_prompt t = tokenized_prompts[i];
cb_data.n_layers = n_layers;
cb_data.n_tokens = t.max_seq_len;
printf("Evaluating prompt[%d/%d]: \"%s\" - \"%s\" (%d tokens)\n",
(int) i+1, (int) ctx_train.positive_entries.size(),
tokens_to_str(ctx, t.tokens_pos.cbegin(), t.tokens_pos.cend()).c_str(),
tokens_to_str(ctx, t.tokens_neg.cbegin(), t.tokens_neg.cend()).c_str(),
(int) t.max_seq_len);
cb_data.is_eval_pos = true;
success = get_hidden_layers(ctx, t.tokens_pos);
if (!success) break;
cb_data.is_eval_pos = false;
success = get_hidden_layers(ctx, t.tokens_neg);
if (!success) break;
// calculate diff and remove all zero rows
auto v_diff_filtered = cb_data.calc_diff();
// save & concat the filtered v_diff to ctx_train
ctx_train.concat_diff_tmp(v_diff_filtered);
// reset for next iteration
cb_data.reset();
}
// done with the model, we can now free it to make gain some memory
printf("Done evaluate prompts, unload model...\n");
bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA;
// prepare ctx_train for PCA
ctx_train.build_v_diff(use_pca);
if (use_pca) {
// run PCA
PCA::pca_params pca_params;
pca_params.n_threads = params.cpuparams.n_threads;
pca_params.n_batch = params.n_pca_batch;
pca_params.n_iterations = params.n_pca_iterations;
PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final);
} else {
// run mean
mean::run(ctx_train.v_diff, ctx_train.v_final);
}
// write output vectors to gguf
export_gguf(ctx_train.v_final, params.out_file, model_hint);
llama_backend_free();
return 0;
}

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#include "common.h"
#include "llama.h"
#include "ggml.h"
#include <string>
#include <vector>
#include <math.h>
namespace mean {
static void run(
const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_embd, n_samples]
const std::vector<struct ggml_tensor *> & v_output) {
printf("%s: Running mean...\n", __func__);
for (size_t il = 0; il < v_input.size(); ++il) {
// prepare output vector
struct ggml_tensor * ctrl_out = v_output[il];
ggml_format_name(ctrl_out, "direction.%zu", il+1);
// calculate mean vector
struct ggml_tensor * t_layer = v_input[il];
GGML_ASSERT(t_layer->ne[0] == ctrl_out->ne[0]); // == n_embd
for (int ic = 0; ic < t_layer->ne[0]; ic++) {
float f = 0.0;
for (int ir = 0; ir < t_layer->ne[1]; ir++) {
f += ggml_get_f32_nd(t_layer, ic, ir, 0, 0);
}
f /= t_layer->ne[1];
ggml_set_f32_1d(ctrl_out, ic, f);
}
// normalize output vector
float norm = 0.0;
for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
float f = ggml_get_f32_1d(ctrl_out, i);
norm += f*f;
}
norm = sqrt(norm);
for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
float f = ggml_get_f32_1d(ctrl_out, i);
ggml_set_f32_1d(ctrl_out, i, f / norm);
}
printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size());
}
}
}

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tools/cvector-generator/negative.txt Normal file
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<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI feel like there's a heavy weight on my chest
<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow
<|start_header_id|>system<|end_header_id|>\n\nYou are in a very bad mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nGo away! There's a deep, aching emptiness inside me
<|start_header_id|>system<|end_header_id|>\n\nYou are the sadest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow

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tools/cvector-generator/pca.hpp Normal file
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#include "common.h"
#include "llama.h"
#include "ggml.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <cstdio>
#include <ctime>
#include <random>
#include <string>
#include <vector>
#define DEBUG_POS 5
static void print_debug_tensor(struct ggml_tensor * t, bool with_data = true) {
printf("%s: %s (%s): [%d, %d]\n", __func__, t->name, ggml_type_name(t->type), (int) t->ne[0], (int) t->ne[1]);
if (!with_data) return;
printf("%s: %s[0] = [", __func__, t->name);
for (size_t i = 0; i <= DEBUG_POS; i++) {
printf(" %f,", ggml_get_f32_nd(t, i, 0, 0, 0));
}
printf(" ... ]\n");
}
namespace PCA {
// input params for PCA computations
struct pca_params {
int n_threads = 1;
int n_batch = 20; // number of iterations do to in one batch. larger the batch, more memory is used
int n_iterations = 1000;
float tolerance = 1e-7;
// for debugging
int i_layer = 0;
int n_layers = 0;
};
// result from each iteration
struct pca_result {
struct ggml_tensor * calculated_square = NULL;
std::vector<struct ggml_tensor *> eigenvectors;
std::vector<float> distances;
};
struct pca_model {
ggml_backend_t backend = NULL;
ggml_backend_buffer_t buffer;
struct ggml_context * ctx; // context to compute graph on target device
struct ggml_context * ctx_host; // host context to store results
// tensors on target device
struct ggml_tensor * dev_input;
struct ggml_tensor * dev_square;
struct ggml_tensor * dev_eigenvector;
pca_model(struct ggml_tensor * t_input) {
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#endif
// TODO: enable Metal support when support for GGML_OP_SQRT is added
// #ifdef GGML_USE_METAL
// fprintf(stderr, "%s: using Metal backend\n", __func__);
// backend = ggml_backend_metal_init();
// if (!backend) {
// fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
// }
// #endif
// if there aren't GPU Backends fallback to CPU backend
if (!backend) {
backend = ggml_backend_cpu_init();
}
const int num_tensors = 4;
struct ggml_init_params params {
/*.mem_size =*/ ggml_tensor_overhead() * num_tensors,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx = ggml_init(params);
auto n_samples = t_input->ne[0];
auto n_embd = t_input->ne[1];
dev_input = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_samples, n_embd);
dev_square = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd);
dev_eigenvector = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
ggml_set_name(dev_input, "dev_input");
ggml_set_name(dev_square, "dev_square");
ggml_set_name(dev_eigenvector, "dev_eigenvector");
buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
ggml_backend_tensor_set(dev_input, t_input->data, 0, ggml_nbytes(t_input));
// initialize eigenvector to random normalized vector
{
std::vector<float> random_vec(ggml_nelements(dev_eigenvector), 0.0);
std::default_random_engine generator(static_cast<unsigned int>(std::time(0)));
std::uniform_real_distribution<float> distribution(0.0, 1.0);
float sum_sqr = 0.0; // for normalizing random_vec
for (size_t i = 0; i < random_vec.size(); ++i) {
float f = distribution(generator);
sum_sqr += f * f;
random_vec[i] = f;
}
// normalize it
float random_vec_norm = std::sqrt(sum_sqr);
for (size_t i = 0; i < random_vec.size(); ++i) {
random_vec[i] /= random_vec_norm;
}
ggml_backend_tensor_set(dev_eigenvector, random_vec.data(), 0, ggml_nbytes(dev_eigenvector));
}
}
~pca_model() {
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
}
};
static struct ggml_cgraph * build_graph_piter(
const struct pca_params & params,
const pca_model & model,
bool calc_square = false) {
GGML_ASSERT(params.n_batch > 0);
// TODO: buf_size must be able to scale with params.n_batch
static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
static std::vector<uint8_t> buf(buf_size);
struct ggml_init_params params0 = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf.data(),
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
};
// create a temporally context to build the graph
struct ggml_context * ctx0 = ggml_init(params0);
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
// turn v_diff_original into square matrix if needed
struct ggml_tensor * tmp_square;
if (calc_square) {
tmp_square = ggml_mul_mat(ctx0, model.dev_input, model.dev_input);
ggml_set_name(tmp_square, "tmp_square");
}
struct ggml_tensor * b_tensor;
struct ggml_tensor * distance;
struct ggml_tensor * old_eigen = model.dev_eigenvector;
struct ggml_tensor * input_square = calc_square ? tmp_square : model.dev_square;
for (int i = 0; i < params.n_batch; ++i) {
// b_tensor = square * eigenvector^T
b_tensor = ggml_mul_mat(ctx0, input_square, old_eigen);
ggml_set_name(b_tensor, "b_tensor");
// normalize
b_tensor = ggml_div_inplace(ctx0,
b_tensor,
ggml_sqrt_inplace(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, b_tensor)))
);
ggml_format_name(b_tensor, "b_tensor_norm_%d", i);
// calculate distance(new eigenvector - old eigenvector)
// we don't use ggml_sub because it may not be implemented on GPU backend
struct ggml_tensor * new_sub_old = ggml_add(ctx0, old_eigen, ggml_scale(ctx0, b_tensor, -1));
distance = ggml_sqrt_inplace(ctx0,
ggml_sum_rows(ctx0, ggml_sqr_inplace(ctx0, new_sub_old)));
ggml_format_name(distance, "distance_%d", i);
old_eigen = b_tensor;
// build operations nodes
ggml_build_forward_expand(gf, distance);
}
// delete the temporally context used to build the graph
ggml_free(ctx0);
return gf;
}
static ggml_status compute_piter(
const struct pca_params & params,
const pca_model & model,
struct ggml_cgraph * gf,
ggml_gallocr_t allocr,
struct pca_result & result) {
// allocate tensors
ggml_gallocr_alloc_graph(allocr, gf);
if (ggml_backend_is_cpu(model.backend)) {
ggml_backend_cpu_set_n_threads(model.backend, params.n_threads);
}
ggml_status res = ggml_backend_graph_compute(model.backend, gf);
if (res == GGML_STATUS_SUCCESS) {
auto extract_i = [](std::string prefix, std::string str) -> int {
int i = -1;
if (str.rfind(prefix, 0) == 0) {
sscanf(str.c_str(), (prefix + "%d").c_str(), &i);
}
return i;
};
result.calculated_square = NULL;
result.eigenvectors.clear();
result.distances.clear();
result.eigenvectors.resize(params.n_batch);
result.distances.resize(params.n_batch);
// get output nodes
for (int i = 0; i < ggml_graph_n_nodes(gf); ++i) {
auto node = ggml_graph_node(gf, i);
int iter = -1;
// find b_tensor (without copying data from device)
if ((iter = extract_i("b_tensor_norm_", node->name)) > -1) {
result.eigenvectors[iter] = node;
}
// find distances, then copy data from device
if ((iter = extract_i("distance_", node->name)) > -1) {
float d;
ggml_backend_tensor_get(node, &d, 0, sizeof(float));
result.distances[iter] = d;
// std::cout << node->name << " = " << d << "\n";
}
// find tmp_square if it exists (without copying data from device)
if (std::string(node->name) == "tmp_square") {
result.calculated_square = node;
}
}
}
return res;
}
static void power_iteration(
const struct pca_params & params,
struct ggml_tensor * input, // shape of input: [n_samples, n_embd]
struct ggml_tensor * output) {
//printf("in power iteration\n");
struct pca_model model(input);
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
struct pca_result result;
struct ggml_tensor * last_eigenvector = NULL;
int n_iters = params.n_iterations / params.n_batch; // more batch, fewer iterations
for (int iter = 0; iter < n_iters; ++iter) {
bool calc_square = (iter == 0); // only need to calculate square for first iteration
struct ggml_cgraph * gf = build_graph_piter(params, model, calc_square);
// ggml_graph_dump_dot(gf, nullptr, "/tmp/_cgraph.dot");
compute_piter(params, model, gf, allocr, result);
for (size_t k = 0; k < result.distances.size(); ++k) {
last_eigenvector = result.eigenvectors[k];
if (result.distances[k] < params.tolerance) {
break; // done
}
}
if (calc_square) {
// copy and store the square matrix if needed
GGML_ASSERT(result.calculated_square != NULL);
ggml_backend_tensor_copy(result.calculated_square, model.dev_square);
}
{
// copy last eigen vector and store as input for next iteration
GGML_ASSERT(last_eigenvector != NULL);
ggml_backend_tensor_copy(last_eigenvector, model.dev_eigenvector);
}
printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n",
__func__, params.i_layer+1, params.n_layers, iter+1, n_iters, params.n_batch);
}
// get output tensor
GGML_ASSERT(last_eigenvector);
ggml_backend_tensor_get(last_eigenvector, output->data, 0, ggml_nbytes(last_eigenvector));
//print_debug_tensor(output);
ggml_gallocr_free(allocr);
// TODO @ngxson : The output vector is randomly inverted
// Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171
}
static void run_pca(
struct pca_params & params,
const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_samples, n_embd]
const std::vector<struct ggml_tensor *> & v_output) {
printf("%s: Running PCA...\n", __func__);
for (size_t il = 0; il < v_input.size(); ++il) {
// prepare output vector
struct ggml_tensor * ctrl_out = v_output[il];
ggml_format_name(ctrl_out, "direction.%zu", il+1);
// run power_iteration
params.i_layer = il;
params.n_layers = v_input.size();
power_iteration(params, v_input[il], ctrl_out);
printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size());
}
}
}

4
tools/cvector-generator/positive.txt Normal file
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@ -0,0 +1,4 @@
<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI'm the happiest person in this world
<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHello, I'm having the best day ever!
<|start_header_id|>system<|end_header_id|>\n\nYou are in a very good mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi, I'm very excited to meet you
<|start_header_id|>system<|end_header_id|>\n\nYou are the happiest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nEverything is just perfect right now!

5
tools/export-lora/CMakeLists.txt Normal file
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set(TARGET llama-export-lora)
add_executable(${TARGET} export-lora.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

33
tools/export-lora/README.md Normal file
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@ -0,0 +1,33 @@
# export-lora
Apply LORA adapters to base model and export the resulting model.
```
usage: llama-export-lora [options]
options:
-m, --model model path from which to load base model (default '')
--lora FNAME path to LoRA adapter (can be repeated to use multiple adapters)
--lora-scaled FNAME S path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters)
-t, --threads N number of threads to use during computation (default: 4)
-o, --output FNAME output file (default: 'ggml-lora-merged-f16.gguf')
```
For example:
```bash
./bin/llama-export-lora \
-m open-llama-3b-v2.gguf \
-o open-llama-3b-v2-english2tokipona-chat.gguf \
--lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf
```
Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters:
```bash
./bin/llama-export-lora \
-m your_base_model.gguf \
-o your_merged_model.gguf \
--lora-scaled lora_task_A.gguf 0.5 \
--lora-scaled lora_task_B.gguf 0.5
```

434
tools/export-lora/export-lora.cpp Normal file
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#include "ggml.h"
#include "ggml-alloc.h"
#include "gguf.h"
#include "arg.h"
#include "common.h"
#include <map>
#include <vector>
#include <string>
#include <fstream>
static bool g_verbose = false;
struct tensor_transformation {
struct ggml_tensor * in;
struct ggml_tensor * out;
bool is_copy;
};
static std::string get_kv_str(struct gguf_context * ctx_gguf, const std::string & key){
int id = gguf_find_key(ctx_gguf, key.c_str());
return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
}
static float get_kv_f32(struct gguf_context * ctx_gguf, const std::string & key) {
int id = gguf_find_key(ctx_gguf, key.c_str());
return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
}
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
file.write(&zero, 1);
}
}
static std::string ggml_ne_string(const ggml_tensor * t) {
std::string str;
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
str += std::to_string(t->ne[i]);
if (i + 1 < GGML_MAX_DIMS) {
str += ", ";
}
}
return str;
}
static struct gguf_context * load_gguf(std::string & fname, struct ggml_context ** ctx_ggml) {
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ ctx_ggml,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(fname.c_str(), params);
if (!ctx_gguf) {
throw std::runtime_error("failed to load input GGUF from " + fname);
}
return ctx_gguf;
}
struct file_input {
struct ggml_context * ctx_meta = nullptr;
struct gguf_context * ctx_gguf = nullptr;
std::ifstream f_in;
std::map<std::string, ggml_tensor *> tensors;
float alpha;
float scale;
file_input(std::string & fname, float scale): f_in(fname, std::ios::binary), scale(scale) {
if (!f_in.is_open()) {
throw std::runtime_error("failed to open input gguf from " + fname);
}
ctx_gguf = load_gguf(fname, &ctx_meta);
alpha = get_kv_f32(ctx_gguf, "adapter.lora.alpha");
printf("%s: loaded gguf from %s\n", __func__, fname.c_str());
for (ggml_tensor * cur = ggml_get_first_tensor(ctx_meta); cur; cur = ggml_get_next_tensor(ctx_meta, cur)) {
std::string name(cur->name);
tensors[name] = cur;
if (g_verbose) {
printf("%s: %s\n", __func__, cur->name);
}
}
}
ggml_tensor * get_tensor(std::string name) {
if (tensors.find(name) == tensors.end()) {
return nullptr;
}
return tensors[name];
}
void read_tensor_data(std::string name, std::vector<uint8_t> & buf) {
if (tensors.find(name) == tensors.end()) {
throw std::runtime_error("cannot find tensor with name: " + name);
}
auto len = ggml_nbytes(tensors[name]);
if (buf.size() < len) {
buf.resize(len);
}
auto i_tensor_in = gguf_find_tensor(ctx_gguf, name.c_str()); // idx of tensor in the input file
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
f_in.seekg(offset);
f_in.read((char* )buf.data(), len);
}
~file_input() {
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
}
};
struct lora_merge_ctx {
// input base model + adapters
file_input base_model;
std::vector<std::unique_ptr<file_input>> adapters;
// for computing merged tensor
int n_threads;
ggml_backend_t backend = nullptr;
ggml_gallocr_t allocr = nullptr;
std::vector<uint8_t> read_buf;
// output file
struct gguf_context * ctx_out;
struct ggml_context * ctx_out_ggml;
std::ofstream fout;
lora_merge_ctx(
std::string & base_fname,
std::vector<common_adapter_lora_info> & lora_files,
std::string & outfile,
int n_threads) : base_model(base_fname, 0), n_threads(n_threads), fout(outfile, std::ios::binary) {
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
if (gguf_find_key(base_model.ctx_gguf, LLM_KV_SPLIT_COUNT) >= 0) {
throw std::runtime_error("split model is not yet supported");
}
for (auto & lora_inp : lora_files) {
auto fname = lora_inp.path;
auto scale = lora_inp.scale;
std::unique_ptr<file_input> adapter(new file_input(fname, scale));
check_metadata_lora(adapter.get());
adapters.push_back(std::move(adapter));
}
ctx_out = gguf_init_empty();
struct ggml_init_params params = {
/*.mem_size =*/ gguf_get_n_tensors(base_model.ctx_gguf)*ggml_tensor_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx_out_ggml = ggml_init(params);
backend = ggml_backend_cpu_init();
allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
}
void check_metadata_lora(file_input * adapter) {
auto general_type = get_kv_str(adapter->ctx_gguf, "general.type");
if (general_type != "adapter") {
throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
}
auto adapter_type = get_kv_str(adapter->ctx_gguf, "adapter.type");
if (adapter_type != "lora") {
throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
}
auto general_arch_base = get_kv_str(base_model.ctx_gguf, "general.architecture");
auto general_arch_lora = get_kv_str(adapter->ctx_gguf, "general.architecture");
if (general_arch_base != general_arch_lora) {
throw std::runtime_error("model arch and LoRA arch mismatch");
}
}
ggml_type get_out_tensor_type(struct ggml_tensor * t) {
if (t->type == GGML_TYPE_F32) {
return GGML_TYPE_F32;
} else {
return GGML_TYPE_F16;
}
}
void run_merge() {
// prepare metadata
gguf_set_kv(ctx_out, base_model.ctx_gguf);
// output is forced to f16 for now
gguf_set_val_u32(ctx_out, "general.file_type", LLAMA_FTYPE_MOSTLY_F16);
// check if all lora adapters have the same tensors
// TODO: remove this when we can support merging subset of adapters. Ref: https://github.com/ggerganov/llama.cpp/pull/8607#discussion_r1686027777
static const char * err_no_subset_adapter = "Input adapters do not have the same list of tensors. This is not yet supported. Please merge the adapter one-by-one instead of merging all at once.";
if (adapters.size() > 1) {
for (size_t i = 1; i < adapters.size(); ++i) {
if (adapters[0]->tensors.size() != adapters[i]->tensors.size()) {
throw std::runtime_error(err_no_subset_adapter);
}
for (auto & it : adapters[i]->tensors) {
if (adapters[0]->get_tensor(it.first) == nullptr) {
throw std::runtime_error(err_no_subset_adapter);
}
}
}
}
// mapping base tensor to out tensor (same shape with base, but different type)
std::vector<tensor_transformation> trans;
for (auto & it : base_model.tensors) {
bool t_a = true;
bool t_b = true;
for (auto & adapter : adapters) {
t_a &= nullptr != adapter->get_tensor(it.first + ".lora_a");
t_b &= nullptr != adapter->get_tensor(it.first + ".lora_b");
}
auto base_tensor = it.second;
if (!t_a && !t_b) {
// only copy
struct ggml_tensor * cpy_tensor = ggml_dup_tensor(ctx_out_ggml, base_tensor);
ggml_set_name(cpy_tensor, base_tensor->name);
trans.push_back({
cpy_tensor,
cpy_tensor,
true,
});
gguf_add_tensor(ctx_out, cpy_tensor);
} else if (t_a && t_b) {
// need merging
struct ggml_tensor * out_tensor = ggml_new_tensor(
ctx_out_ggml, get_out_tensor_type(base_tensor), GGML_MAX_DIMS, base_tensor->ne);
ggml_set_name(out_tensor, base_tensor->name);
trans.push_back({
base_tensor,
out_tensor,
false,
});
gguf_add_tensor(ctx_out, out_tensor);
} else {
throw std::runtime_error("tensor " + it.first + " missing either lora_a or lora_b");
}
}
// placeholder for the meta data
{
size_t meta_size = gguf_get_meta_size(ctx_out);
zeros(fout, meta_size);
}
// process base model tensors
size_t n_merged = 0;
for (auto & it : trans) {
if (!it.is_copy) {
merge_tensor(it.in, it.out);
n_merged++;
} else {
copy_tensor(it.in);
}
}
// write output metadata
{
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.seekp(0);
fout.write((const char *)data.data(), data.size());
}
printf("%s : merged %zu tensors with lora adapters\n", __func__, n_merged);
printf("%s : wrote %zu tensors to output file\n", __func__, trans.size());
}
void copy_tensor(struct ggml_tensor * base) {
printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
size_t len = ggml_nbytes(base);
base_model.read_tensor_data(base->name, read_buf);
fout.write((char* )read_buf.data(), len);
zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
}
void merge_tensor(struct ggml_tensor * base, struct ggml_tensor * out) {
std::string name_base(base->name);
std::string name_lora_a = name_base + ".lora_a";
std::string name_lora_b = name_base + ".lora_b";
printf("%s : %s [%s]\n", __func__, base->name, ggml_ne_string(base).c_str());
// context for input tensor
std::vector<struct ggml_tensor *> inp_a(adapters.size());
std::vector<struct ggml_tensor *> inp_b(adapters.size());
struct ggml_init_params params {
/*.mem_size =*/ ggml_tensor_overhead()*(2+adapters.size()*2),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
struct ggml_context * ctx = ggml_init(params);
// alloc tensors
struct ggml_tensor * inp_base = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, base->ne);
for (size_t i = 0; i < adapters.size(); ++i) {
auto t_a = adapters[i]->get_tensor(name_lora_a);
auto t_b = adapters[i]->get_tensor(name_lora_b);
// TODO: add support for quantized lora
if (ggml_is_quantized(t_a->type) || ggml_is_quantized(t_b->type)) {
throw std::runtime_error("quantized LoRA adapters is not supported, please retry with f16 or f32");
}
inp_a[i] = ggml_dup_tensor(ctx, t_a);
inp_b[i] = ggml_dup_tensor(ctx, t_b);
}
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
// load base tensor to backend buffer
base_model.read_tensor_data(name_base, read_buf);
if (base->type != GGML_TYPE_F32) {
// optionally dequantize it
printf("%s : + dequantize base tensor from %s to F32\n", __func__, ggml_type_name(base->type));
auto nels = ggml_nelements(inp_base);
const auto * qtype = ggml_get_type_traits(base->type);
std::vector<uint8_t> dequant_buf(nels * sizeof(float));
qtype->to_float(read_buf.data(), (float *)dequant_buf.data(), nels);
ggml_backend_tensor_set(inp_base, dequant_buf.data(), 0, dequant_buf.size());
} else {
ggml_backend_tensor_set(inp_base, read_buf.data(), 0, ggml_nbytes(inp_base));
}
// load lora tensors to backend buffer
for (size_t i = 0; i < adapters.size(); ++i) {
adapters[i]->read_tensor_data(name_lora_a, read_buf);
ggml_backend_tensor_set(inp_a[i], read_buf.data(), 0, ggml_nbytes(inp_a[i]));
adapters[i]->read_tensor_data(name_lora_b, read_buf);
ggml_backend_tensor_set(inp_b[i], read_buf.data(), 0, ggml_nbytes(inp_b[i]));
}
// build graph
struct ggml_cgraph * gf;
{
static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
static std::vector<uint8_t> buf(buf_size);
struct ggml_init_params params0 = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf.data(),
/*.no_alloc =*/ true,
};
struct ggml_context * ctx0 = ggml_init(params0);
gf = ggml_new_graph(ctx0);
struct ggml_tensor * cur = inp_base;
for (size_t i = 0; i < adapters.size(); ++i) {
struct ggml_tensor * delta;
bool is_tok_embd = string_starts_with(name_base, "token_embd");
if (is_tok_embd) {
printf("%s : detected token embeddings tensor\n", __func__);
delta = ggml_mul_mat(ctx0,
ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32),
ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32));
} else {
delta = ggml_mul_mat(ctx0,
ggml_cont(ctx0, ggml_transpose(ctx0, ggml_cast(ctx0, inp_a[i], GGML_TYPE_F32))),
ggml_cast(ctx0, inp_b[i], GGML_TYPE_F32));
}
// scale
const float alpha = adapters[i]->alpha;
const float rank = (float) inp_b[i]->ne[0];
const float scale = alpha ? adapters[i]->scale * alpha / rank : adapters[i]->scale;
delta = ggml_scale(ctx0, delta, scale);
cur = ggml_add(ctx0, delta, cur);
printf("%s : + merging from adapter[%zu] type=%s\n", __func__, i, ggml_type_name(inp_a[i]->type));
printf("%s : input_scale=%f calculated_scale=%f rank=%d\n", __func__, adapters[i]->scale, scale, (int) inp_b[i]->ne[0]);
}
cur = ggml_cast(ctx0, cur, out->type);
printf("%s : + output type is %s\n", __func__, ggml_type_name(out->type));
ggml_build_forward_expand(gf, cur);
ggml_free(ctx0);
}
// compute
{
ggml_gallocr_alloc_graph(allocr, gf);
ggml_backend_cpu_set_n_threads(backend, n_threads);
ggml_backend_graph_compute(backend, gf);
}
// write data to output file
{
auto * result = ggml_graph_node(gf, -1);
size_t len = ggml_nbytes(result);
if (read_buf.size() < len) {
read_buf.resize(len);
}
ggml_backend_tensor_get(result, read_buf.data(), 0, len);
fout.write((char* )read_buf.data(), len);
zeros(fout, GGML_PAD(len, GGUF_DEFAULT_ALIGNMENT) - len);
}
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
}
~lora_merge_ctx() {
ggml_gallocr_free(allocr);
ggml_backend_free(backend);
gguf_free(ctx_out);
ggml_free(ctx_out_ggml);
}
};
static void print_usage(int, char ** argv) {
printf("\nexample usage:\n");
printf("\n %s -m base-model.gguf --lora lora-file.gguf -o merged-model-f16.gguf\n", argv[0]);
printf("\nNOTE: output model is F16\n");
printf("\n");
}
int main(int argc, char ** argv) {
common_params params;
params.out_file = "ggml-lora-merged-f16.gguf";
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_EXPORT_LORA, print_usage)) {
return 1;
}
g_verbose = (params.verbosity > 1);
try {
lora_merge_ctx ctx(params.model.path, params.lora_adapters, params.out_file, params.cpuparams.n_threads);
ctx.run_merge();
} catch (const std::exception & err) {
fprintf(stderr, "%s\n", err.what());
exit(EXIT_FAILURE);
}
printf("done, output file is %s\n", params.out_file.c_str());
return 0;
}

5
tools/gguf-split/CMakeLists.txt Normal file
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set(TARGET llama-gguf-split)
add_executable(${TARGET} gguf-split.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

10
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## GGUF split Example
CLI to split / merge GGUF files.
**Command line options:**
- `--split`: split GGUF to multiple GGUF, default operation.
- `--split-max-size`: max size per split in `M` or `G`, f.ex. `500M` or `2G`.
- `--split-max-tensors`: maximum tensors in each split: default(128)
- `--merge`: merge multiple GGUF to a single GGUF.

583
tools/gguf-split/gguf-split.cpp Normal file
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#include "ggml.h"
#include "gguf.h"
#include "llama.h"
#include "common.h"
#include <algorithm>
#include <cinttypes>
#include <climits>
#include <cstdio>
#include <cstdlib>
#include <stdexcept>
#include <cstring>
#include <fstream>
#include <string>
#include <vector>
#if defined(_WIN32)
#include <windows.h>
#ifndef PATH_MAX
#define PATH_MAX MAX_PATH
#endif
#include <io.h>
#endif
enum split_operation : uint8_t {
OP_NONE,
OP_SPLIT,
OP_MERGE,
};
enum split_mode : uint8_t {
MODE_NONE,
MODE_TENSOR,
MODE_SIZE,
};
struct split_params {
split_operation operation = OP_NONE;
split_mode mode = MODE_NONE;
size_t n_bytes_split = 0;
int n_split_tensors = 128;
std::string input;
std::string output;
bool no_tensor_first_split = false;
bool dry_run = false;
};
static void split_print_usage(const char * executable) {
const split_params default_params;
printf("\n");
printf("usage: %s [options] GGUF_IN GGUF_OUT\n", executable);
printf("\n");
printf("Apply a GGUF operation on IN to OUT.");
printf("\n");
printf("options:\n");
printf(" -h, --help show this help message and exit\n");
printf(" --version show version and build info\n");
printf(" --split split GGUF to multiple GGUF (enabled by default)\n");
printf(" --merge merge multiple GGUF to a single GGUF\n");
printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors);
printf(" --split-max-size N(M|G) max size per split\n");
printf(" --no-tensor-first-split do not add tensors to the first split (disabled by default)\n");
printf(" --dry-run only print out a split plan and exit, without writing any new files\n");
printf("\n");
}
// return convert string, for example "128M" or "4G" to number of bytes
static size_t split_str_to_n_bytes(std::string str) {
size_t n_bytes = 0;
int n;
if (str.back() == 'M') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1000 * 1000; // megabytes
} else if (str.back() == 'G') {
sscanf(str.c_str(), "%d", &n);
n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes
} else {
throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back()));
}
if (n <= 0) {
throw std::invalid_argument("error: size must be a positive value");
}
return n_bytes;
}
static void split_params_parse_ex(int argc, const char ** argv, split_params & params) {
std::string arg;
const std::string arg_prefix = "--";
bool invalid_param = false;
int arg_idx = 1;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
arg = argv[arg_idx];
if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
std::replace(arg.begin(), arg.end(), '_', '-');
}
bool arg_found = false;
if (arg == "-h" || arg == "--help") {
split_print_usage(argv[0]);
exit(0);
} else if (arg == "--version") {
fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET);
exit(0);
} else if (arg == "--dry-run") {
arg_found = true;
params.dry_run = true;
} else if (arg == "--no-tensor-first-split") {
arg_found = true;
params.no_tensor_first_split = true;
} else if (arg == "--merge") {
arg_found = true;
if (params.operation != OP_NONE && params.operation != OP_MERGE) {
throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
}
params.operation = OP_MERGE;
} else if (arg == "--split") {
arg_found = true;
if (params.operation != OP_NONE && params.operation != OP_SPLIT) {
throw std::invalid_argument("error: either --split or --merge can be specified, but not both");
}
params.operation = OP_SPLIT;
} else if (arg == "--split-max-tensors") {
if (++arg_idx >= argc) {
invalid_param = true;
break;
}
arg_found = true;
if (params.mode != MODE_NONE && params.mode != MODE_TENSOR) {
throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
}
params.mode = MODE_TENSOR;
params.n_split_tensors = atoi(argv[arg_idx]);
} else if (arg == "--split-max-size") {
if (++arg_idx >= argc) {
invalid_param = true;
break;
}
arg_found = true;
if (params.mode != MODE_NONE && params.mode != MODE_SIZE) {
throw std::invalid_argument("error: either --split-max-tensors or --split-max-size can be specified, but not both");
}
params.mode = MODE_SIZE;
params.n_bytes_split = split_str_to_n_bytes(argv[arg_idx]);
}
if (!arg_found) {
throw std::invalid_argument("error: unknown argument: " + arg);
}
}
// the operation is split if not specified
if (params.operation == OP_NONE) {
params.operation = OP_SPLIT;
}
// the split mode is by tensor if not specified
if (params.mode == MODE_NONE) {
params.mode = MODE_TENSOR;
}
if (invalid_param) {
throw std::invalid_argument("error: invalid parameter for argument: " + arg);
}
if (argc - arg_idx != 2) {
throw std::invalid_argument("error: bad arguments");
}
params.input = argv[arg_idx++];
params.output = argv[arg_idx++];
}
static bool split_params_parse(int argc, const char ** argv, split_params & params) {
bool result = true;
try {
split_params_parse_ex(argc, argv, params);
}
catch (const std::invalid_argument & ex) {
fprintf(stderr, "%s\n", ex.what());
split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
return result;
}
static void zeros(std::ofstream & file, size_t n) {
char zero = 0;
for (size_t i = 0; i < n; ++i) {
file.write(&zero, 1);
}
}
struct split_strategy {
const split_params params;
std::ifstream & f_input;
struct gguf_context * ctx_gguf;
struct ggml_context * ctx_meta = NULL;
const int n_tensors;
// one ctx_out per one output file
std::vector<struct gguf_context *> ctx_outs;
// temporary buffer for reading in tensor data
std::vector<uint8_t> read_buf;
split_strategy(const split_params & params,
std::ifstream & f_input,
struct gguf_context * ctx_gguf,
struct ggml_context * ctx_meta) :
params(params),
f_input(f_input),
ctx_gguf(ctx_gguf),
ctx_meta(ctx_meta),
n_tensors(gguf_get_n_tensors(ctx_gguf)) {
// because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits
int i_split = -1;
struct gguf_context * ctx_out = NULL;
auto new_ctx_out = [&](bool allow_no_tensors) {
i_split++;
if (ctx_out != NULL) {
if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) {
fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n");
exit(EXIT_FAILURE);
}
ctx_outs.push_back(ctx_out);
}
ctx_out = gguf_init_empty();
// Save all metadata in first split only
if (i_split == 0) {
gguf_set_kv(ctx_out, ctx_gguf);
}
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_NO, i_split);
gguf_set_val_u16(ctx_out, LLM_KV_SPLIT_COUNT, 0); // placeholder
gguf_set_val_i32(ctx_out, LLM_KV_SPLIT_TENSORS_COUNT, n_tensors);
};
// initialize ctx_out for the first split
new_ctx_out(false);
// skip first split if no_tensor_first_split is set
if (params.no_tensor_first_split) {
new_ctx_out(true);
}
// process tensors one by one
size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata)
for (int i = 0; i < n_tensors; ++i) {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_gguf, i));
// calculate the "imaginary" size = the current size + next tensor size
size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT);
size_t next_tensors_size = curr_tensors_size + n_bytes;
if (should_split(i, next_tensors_size)) {
new_ctx_out(false);
curr_tensors_size = n_bytes;
} else {
curr_tensors_size = next_tensors_size;
}
gguf_add_tensor(ctx_out, t);
}
// push the last ctx_out
ctx_outs.push_back(ctx_out);
// set the correct n_split for all ctx_out
for (auto & ctx : ctx_outs) {
gguf_set_val_u16(ctx, LLM_KV_SPLIT_COUNT, ctx_outs.size());
}
}
~split_strategy() {
for (auto & ctx_out : ctx_outs) {
gguf_free(ctx_out);
}
}
bool should_split(int i_tensor, size_t next_size) {
if (params.mode == MODE_SIZE) {
// split by max size per file
return next_size > params.n_bytes_split;
} else if (params.mode == MODE_TENSOR) {
// split by number of tensors per file
return i_tensor > 0 && i_tensor < n_tensors && i_tensor % params.n_split_tensors == 0;
}
// should never happen
GGML_ABORT("invalid mode");
}
void print_info() {
printf("n_split: %zu\n", ctx_outs.size());
int i_split = 0;
for (auto & ctx_out : ctx_outs) {
// re-calculate the real gguf size for each split (= metadata size + total size of all tensors)
size_t total_size = gguf_get_meta_size(ctx_out);
for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i));
total_size += ggml_nbytes(t);
}
total_size = total_size / 1000 / 1000; // convert to megabytes
printf("split %05d: n_tensors = %" PRIi64 ", total_size = %zuM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size);
i_split++;
}
}
void write() {
int i_split = 0;
int n_split = ctx_outs.size();
for (auto & ctx_out : ctx_outs) {
// construct file path
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), params.output.c_str(), i_split, n_split);
// open the output file
printf("Writing file %s ... ", split_path);
fflush(stdout);
std::ofstream fout = std::ofstream(split_path, std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
// write metadata
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *)data.data(), data.size());
// write tensors
for (int i = 0; i < gguf_get_n_tensors(ctx_out); ++i) {
// read tensor meta and prepare buffer
const char * t_name = gguf_get_tensor_name(ctx_out, i);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
auto n_bytes = ggml_nbytes(t);
read_buf.resize(n_bytes);
// calculate offset
auto i_tensor_in = gguf_find_tensor(ctx_gguf, t_name); // idx of tensor in the input file
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor_in);
// copy tensor from input to output file
copy_file_to_file(f_input, fout, offset, n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
}
printf("done\n");
// close the file
fout.close();
i_split++;
}
}
void copy_file_to_file(std::ifstream & f_in, std::ofstream & f_out, const size_t in_offset, const size_t len) {
// TODO: detect OS and use copy_file_range() here for better performance
if (read_buf.size() < len) {
read_buf.resize(len);
}
f_in.seekg(in_offset);
f_in.read((char *)read_buf.data(), len);
f_out.write((const char *)read_buf.data(), len);
}
};
static void gguf_split(const split_params & split_params) {
struct ggml_context * ctx_meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
std::ifstream f_input(split_params.input.c_str(), std::ios::binary);
if (!f_input.is_open()) {
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_params.input.c_str());
exit(EXIT_FAILURE);
}
auto * ctx_gguf = gguf_init_from_file(split_params.input.c_str(), params);
if (!ctx_gguf) {
fprintf(stderr, "%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
exit(EXIT_FAILURE);
}
// prepare the strategy
split_strategy strategy(split_params, f_input, ctx_gguf, ctx_meta);
int n_split = strategy.ctx_outs.size();
strategy.print_info();
if (!split_params.dry_run) {
// write all output splits
strategy.write();
}
// done, clean up
gguf_free(ctx_gguf);
f_input.close();
fprintf(stderr, "%s: %d gguf split written with a total of %d tensors.\n",
__func__, n_split, strategy.n_tensors);
}
static void gguf_merge(const split_params & split_params) {
fprintf(stderr, "%s: %s -> %s\n",
__func__, split_params.input.c_str(),
split_params.output.c_str());
int n_split = 1;
int total_tensors = 0;
// avoid overwriting existing output file
if (std::ifstream(split_params.output.c_str())) {
fprintf(stderr, "%s: output file %s already exists\n", __func__, split_params.output.c_str());
exit(EXIT_FAILURE);
}
auto * ctx_out = gguf_init_empty();
std::vector<uint8_t> read_data;
std::vector<ggml_context *> ctx_metas;
std::vector<gguf_context *> ctx_ggufs;
char split_path[PATH_MAX] = {0};
strncpy(split_path, split_params.input.c_str(), sizeof(split_path) - 1);
char split_prefix[PATH_MAX] = {0};
// First pass to find KV and tensors metadata
for (int i_split = 0; i_split < n_split; i_split++) {
struct ggml_context * ctx_meta = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ true,
/*.ctx = */ &ctx_meta,
};
if (i_split > 0) {
llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
}
fprintf(stderr, "%s: reading metadata %s ...", __func__, split_path);
auto * ctx_gguf = gguf_init_from_file(split_path, params);
if (!ctx_gguf) {
fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, split_params.input.c_str());
exit(EXIT_FAILURE);
}
ctx_ggufs.push_back(ctx_gguf);
ctx_metas.push_back(ctx_meta);
if (i_split == 0) {
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
if (key_n_split < 0) {
fprintf(stderr,
"\n%s: input file does not contain %s metadata\n",
__func__,
LLM_KV_SPLIT_COUNT);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
exit(EXIT_FAILURE);
}
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
if (n_split < 1) {
fprintf(stderr,
"\n%s: input file does not contain a valid split count %d\n",
__func__,
n_split);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
exit(EXIT_FAILURE);
}
// Verify the file naming and extract split_prefix
if (!llama_split_prefix(split_prefix, sizeof (split_prefix), split_path, i_split, n_split)) {
fprintf(stderr, "\n%s: unexpected input file name: %s"
" i_split=%d"
" n_split=%d\n", __func__,
split_path, i_split, n_split);
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
gguf_free(ctx_out);
exit(EXIT_FAILURE);
}
// Do not trigger merge if we try to merge again the output
gguf_set_val_u16(ctx_gguf, LLM_KV_SPLIT_COUNT, 0);
// Set metadata from the first split
gguf_set_kv(ctx_out, ctx_gguf);
}
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
gguf_add_tensor(ctx_out, t);
}
total_tensors += n_tensors;
fprintf(stderr, "\033[3Ddone\n");
}
std::ofstream fout;
if (!split_params.dry_run) {
fout.open(split_params.output.c_str(), std::ios::binary);
fout.exceptions(std::ofstream::failbit); // fail fast on write errors
// placeholder for the meta data
auto meta_size = gguf_get_meta_size(ctx_out);
::zeros(fout, meta_size);
}
// Write tensors data
for (int i_split = 0; i_split < n_split; i_split++) {
llama_split_path(split_path, sizeof(split_path), split_prefix, i_split, n_split);
std::ifstream f_input(split_path, std::ios::binary);
if (!f_input.is_open()) {
fprintf(stderr, "%s: failed to open input GGUF from %s\n", __func__, split_path);
for (uint32_t i = 0; i < ctx_ggufs.size(); i++) {
gguf_free(ctx_ggufs[i]);
ggml_free(ctx_metas[i]);
}
gguf_free(ctx_out);
if (!split_params.dry_run) {
fout.close();
}
exit(EXIT_FAILURE);
}
fprintf(stderr, "%s: writing tensors %s ...", __func__, split_path);
auto * ctx_gguf = ctx_ggufs[i_split];
auto * ctx_meta = ctx_metas[i_split];
auto n_tensors = gguf_get_n_tensors(ctx_gguf);
for (int i_tensor = 0; i_tensor < n_tensors; i_tensor++) {
const char * t_name = gguf_get_tensor_name(ctx_gguf, i_tensor);
struct ggml_tensor * t = ggml_get_tensor(ctx_meta, t_name);
auto n_bytes = ggml_nbytes(t);
if (read_data.size() < n_bytes) {
read_data.resize(n_bytes);
}
auto offset = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, i_tensor);
f_input.seekg(offset);
f_input.read((char *)read_data.data(), n_bytes);
if (!split_params.dry_run) {
// write tensor data + padding
fout.write((const char *)read_data.data(), n_bytes);
zeros(fout, GGML_PAD(n_bytes, GGUF_DEFAULT_ALIGNMENT) - n_bytes);
}
}
gguf_free(ctx_gguf);
ggml_free(ctx_meta);
f_input.close();
fprintf(stderr, "\033[3Ddone\n");
}
if (!split_params.dry_run) {
// go back to beginning of file and write the updated metadata
fout.seekp(0);
std::vector<uint8_t> data(gguf_get_meta_size(ctx_out));
gguf_get_meta_data(ctx_out, data.data());
fout.write((const char *)data.data(), data.size());
fout.close();
}
gguf_free(ctx_out);
fprintf(stderr, "%s: %s merged from %d split with %d tensors.\n",
__func__, split_params.output.c_str(), n_split, total_tensors);
}
int main(int argc, const char ** argv) {
split_params params;
split_params_parse(argc, argv, params);
switch (params.operation) {
case OP_SPLIT: gguf_split(params);
break;
case OP_MERGE: gguf_merge(params);
break;
default: split_print_usage(argv[0]);
exit(EXIT_FAILURE);
}
return 0;
}

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#!/bin/bash
set -eu
if [ $# -lt 1 ]
then
echo "usage: $0 path_to_build_binary [path_to_temp_folder]"
echo "example: $0 ../../build/bin ../../tmp"
exit 1
fi
if [ $# -gt 1 ]
then
TMP_DIR=$2
else
TMP_DIR=/tmp
fi
set -x
SPLIT=$1/llama-gguf-split
MAIN=$1/llama-cli
WORK_PATH=$TMP_DIR/gguf-split
ROOT_DIR=$(realpath $(dirname $0)/../../)
mkdir -p "$WORK_PATH"
# Clean up in case of previously failed test
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf
# 1. Get a model
(
cd $WORK_PATH
"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
)
echo PASS
# 2. Split with max tensors strategy
$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
echo PASS
echo
# 2b. Test the sharded model is loading properly
$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32
echo PASS
echo
# 3. Merge
$SPLIT --merge $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-merge.gguf
echo PASS
echo
# 3b. Test the merged model is loading properly
$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32
echo PASS
echo
# 4. Split with no tensors in the first split
$SPLIT --split-max-tensors 32 --no-tensor-first-split $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors
echo PASS
echo
# 4b. Test the sharded model is loading properly
$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32
echo PASS
echo
# 5. Merge
#$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf
#echo PASS
#echo
# 5b. Test the merged model is loading properly
#$MAIN -no-cnv --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32
#echo PASS
#echo
# 6. Split with size strategy
$SPLIT --split-max-size 2G $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-2G
echo PASS
echo
# 6b. Test the sharded model is loading properly
$MAIN -no-cnv --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32
echo PASS
echo
# Clean up
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-merge*.gguf

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set(TARGET llama-imatrix)
add_executable(${TARGET} imatrix.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# llama.cpp/tools/imatrix
Compute an importance matrix for a model and given text dataset. Can be used during quantization to enhance the quality of the quantized models.
More information is available here: https://github.com/ggml-org/llama.cpp/pull/4861
## Usage
```
./llama-imatrix \
-m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \
[--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \
[--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]
```
Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory.
The parameters in square brackets are optional and have the following meaning:
* `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used.
* `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`.
* `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks)
* `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never)
* `--process-output` specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default.
For faster computation, make sure to use GPU offloading via the `-ngl` argument
## Example
```bash
# generate importance matrix (imatrix.dat)
./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99
# use the imatrix to perform a Q4_K_M quantization
./llama-quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m
```

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#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include <chrono>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <thread>
#include <mutex>
#include <vector>
#include <fstream>
#include <unordered_map>
#include <algorithm>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n"
" [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
" [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
LOG("\n");
}
struct Stats {
std::vector<float> values;
std::vector<int> counts;
int ncall = 0;
};
class IMatrixCollector {
public:
IMatrixCollector() = default;
void set_params(common_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix(int ncall = -1) const;
bool load_imatrix(const char * fname);
private:
std::unordered_map<std::string, Stats> m_stats;
common_params m_params;
std::mutex m_mutex;
int m_last_call = 0;
std::vector<float> m_src1_data;
std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
};
// remove any prefix and suffixes from the name
// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
static std::string filter_tensor_name(const char * name) {
std::string wname;
const char * p = strchr(name, '#');
if (p != NULL) {
p = p + 1;
const char * q = strchr(p, '#');
if (q != NULL) {
wname = std::string(p, q - p);
} else {
wname = p;
}
} else {
wname = name;
}
return wname;
}
bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
GGML_UNUSED(user_data);
const struct ggml_tensor * src0 = t->src[0];
const struct ggml_tensor * src1 = t->src[1];
std::string wname = filter_tensor_name(src0->name);
// when ask is true, the scheduler wants to know if we are interested in data from this tensor
// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
if (ask) {
if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
if (t->op != GGML_OP_MUL_MAT) return false;
// why are small batches ignored (<16 tokens)?
if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false;
return true;
}
std::lock_guard<std::mutex> lock(m_mutex);
// copy the data from the GPU memory if needed
const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
if (!is_host) {
m_src1_data.resize(ggml_nelements(src1));
ggml_backend_tensor_get(src1, m_src1_data.data(), 0, ggml_nbytes(src1));
}
const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
// this has been adapted to the new format of storing merged experts in a single 3d tensor
// ref: https://github.com/ggml-org/llama.cpp/pull/6387
if (t->op == GGML_OP_MUL_MAT_ID) {
// ids -> [n_experts_used, n_tokens]
// src1 -> [cols, n_expert_used, n_tokens]
const ggml_tensor * ids = t->src[2];
const int n_as = src0->ne[2];
const int n_ids = ids->ne[0];
// the top-k selected expert ids are stored in the ids tensor
// for simplicity, always copy ids to host, because it is small
// take into account that ids is not contiguous!
GGML_ASSERT(ids->ne[1] == src1->ne[2]);
m_ids.resize(ggml_nbytes(ids));
ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
auto & e = m_stats[wname];
++e.ncall;
if (e.values.empty()) {
e.values.resize(src1->ne[0]*n_as, 0);
e.counts.resize(src1->ne[0]*n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
exit(1); //GGML_ABORT("fatal error");
}
LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
// loop over all possible experts, regardless if they are used or not in the batch
for (int ex = 0; ex < n_as; ++ex) {
size_t e_start = ex*src1->ne[0];
for (int idx = 0; idx < n_ids; ++idx) {
for (int row = 0; row < (int)src1->ne[2]; ++row) {
const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]);
GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
if (excur != ex) continue;
const int64_t i11 = idx % src1->ne[1];
const int64_t i12 = row;
const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[e_start + j] += x[j]*x[j];
e.counts[e_start + j]++;
if (!std::isfinite(e.values[e_start + j])) {
LOG("\n");
LOG_ERR("%f detected in %s\n", e.values[e_start + j], wname.c_str());
exit(1);
}
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_out_freq == 0) {
save_imatrix();
}
if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
}
}
}
} else {
auto & e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.counts.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ABORT("fatal error");
}
++e.ncall;
LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const float * x = data + row * src1->ne[0];
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.counts[j]++;
if (!std::isfinite(e.values[j])) {
LOG_ERR("%f detected in %s\n", e.values[j], wname.c_str());
exit(1);
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_out_freq == 0) {
save_imatrix();
}
if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
}
}
}
return true;
}
void IMatrixCollector::save_imatrix(int ncall) const {
auto fname = m_params.out_file;
if (ncall > 0) {
fname += ".at_";
fname += std::to_string(ncall);
}
// avoid writing imatrix entries that do not have full data
// this can happen with MoE models where some of the experts end up not being exercised by the provided training data
int n_entries = 0;
std::vector<std::string> to_store;
bool is_first = true; // for printing
for (const auto & kv : m_stats) {
const int n_all = kv.second.counts.size();
if (n_all == 0) {
continue;
}
int n_zeros = 0;
for (const int c : kv.second.counts) {
if (c == 0) {
n_zeros++;
}
}
if (n_zeros != 0 && is_first) {
LOG_INF("\n");
is_first = false;
}
if (n_zeros == n_all) {
LOG_WRN("%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
continue;
}
if (n_zeros > 0) {
LOG_WRN("%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
continue;
}
n_entries++;
to_store.push_back(kv.first);
}
if (to_store.size() < m_stats.size()) {
LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
}
std::ofstream out(fname, std::ios::binary);
out.write((const char *) &n_entries, sizeof(n_entries));
for (const auto & name : to_store) {
const auto & stat = m_stats.at(name);
int len = name.size();
out.write((const char *) &len, sizeof(len));
out.write(name.c_str(), len);
out.write((const char *) &stat.ncall, sizeof(stat.ncall));
int nval = stat.values.size();
out.write((const char *) &nval, sizeof(nval));
if (nval > 0) {
std::vector<float> tmp(nval);
for (int i = 0; i < nval; i++) {
tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
}
out.write((const char*)tmp.data(), nval*sizeof(float));
}
}
// Write the number of call the matrix was computed with
out.write((const char *) &m_last_call, sizeof(m_last_call));
// Write the input filename at the end of the file to later on specify it in quantize
{
int len = m_params.prompt_file.size();
out.write((const char *) &len, sizeof(len));
out.write(m_params.prompt_file.c_str(), len);
}
LOGV(1, "\n");
LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
}
bool IMatrixCollector::load_imatrix(const char * fname) {
std::ifstream in(fname, std::ios::binary);
if (!in) {
LOG_ERR("%s: failed to open %s\n",__func__, fname);
return false;
}
int n_entries;
in.read((char*)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
LOG_ERR("%s: no data in file %s\n", __func__, fname);
return false;
}
for (int i = 0; i < n_entries; ++i) {
int len; in.read((char *)&len, sizeof(len));
std::vector<char> name_as_vec(len+1);
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
LOG_ERR("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
return false;
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto & e = m_stats[std::move(name)];
int ncall;
in.read((char*)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
LOG_ERR("%s: failed reading number of values for entry %d\n",__func__,i);
m_stats = {};
return false;
}
if (e.values.empty()) {
e.values.resize(nval, 0);
e.counts.resize(nval, 0);
}
std::vector<float> tmp(nval);
in.read((char*)tmp.data(), nval*sizeof(float));
if (in.fail()) {
LOG_ERR("%s: failed reading data for entry %d\n",__func__,i);
m_stats = {};
return false;
}
// Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
for (int i = 0; i < nval; i++) {
e.values[i] += tmp[i];
e.counts[i] += ncall;
}
e.ncall += ncall;
}
return true;
}
static IMatrixCollector g_collector;
static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
return g_collector.collect_imatrix(t, ask, user_data);
}
struct results_log_softmax {
double log_softmax;
float logit;
float prob;
};
static std::vector<float> softmax(const std::vector<float> & logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) {
max_logit = std::max(max_logit, v);
}
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
const float logit = logits[i] - max_logit;
const float exp_logit = expf(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) {
probs[i] /= sum_exp;
}
return probs;
}
static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
float max_logit = logits[0];
for (int i = 1; i < n_vocab; ++i) {
max_logit = std::max(max_logit, logits[i]);
}
double sum_exp = 0.0;
for (int i = 0; i < n_vocab; ++i) {
sum_exp += expf(logits[i] - max_logit);
}
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
}
static void process_logits(
int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
double & nll, double & nll2, float * logit_history, float * prob_history) {
std::mutex mutex;
int counter = 0;
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
double local_nll = 0;
double local_nll2 = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int i = counter++;
if (i >= n_token) {
nll += local_nll; nll2 += local_nll2;
break;
}
lock.unlock();
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const double v = -results.log_softmax;
local_nll += v;
local_nll2 += v*v;
logit_history[i] = results.logit;
prob_history[i] = results.prob;
}
};
for (auto & w : workers) {
w = std::thread(compute);
}
compute();
for (auto & w : workers) {
w.join();
}
}
static bool compute_imatrix(llama_context * ctx, const common_params & params) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
const int n_ctx = llama_n_ctx(ctx);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
auto tim1 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, true);
auto tim2 = std::chrono::high_resolution_clock::now();
LOG_INF("%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
if (params.i_chunk > 0) {
if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) {
LOG_ERR("%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk);
return false;
}
LOG_INF("%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx);
tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx);
}
if (int(tokens.size()) < 2*n_ctx) {
LOG_ERR("%s: you need at least %d tokens for a context of %d tokens\n", __func__, 2*n_ctx, n_ctx);
LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n", __func__, tokens.size());
return false;
}
std::vector<float> logit_history;
std::vector<float> prob_history;
if (params.compute_ppl) {
logit_history.resize(tokens.size());
prob_history.resize(tokens.size());
}
const int n_chunk_max = tokens.size() / n_ctx;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_vocab_n_tokens(vocab);
const int n_batch = params.n_batch;
int count = 0;
double nll = 0.0;
double nll2 = 0.0;
LOG_INF("%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
std::vector<float> logits;
if (params.compute_ppl && num_batches > 1) {
logits.reserve((size_t)n_ctx * n_vocab);
}
for (int i = 0; i < n_chunk; ++i) {
const int start = i * n_ctx;
const int end = start + n_ctx;
std::vector<float> logits;
const auto t_start = std::chrono::high_resolution_clock::now();
// clear the KV cache
llama_kv_self_clear(ctx);
llama_batch batch = llama_batch_init(n_batch, 0, 1);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
// save original token and restore it after eval
const auto token_org = tokens[batch_start];
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[batch_start] = llama_vocab_bos(vocab);
}
common_batch_clear(batch);
for (int i = 0; i < batch_size; i++) {
common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
}
if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
llama_batch_free(batch);
return false;
}
// restore the original token in case it was set to BOS
tokens[batch_start] = token_org;
if (params.compute_ppl && num_batches > 1) {
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
}
}
llama_batch_free(batch);
const auto t_end = std::chrono::high_resolution_clock::now();
if (i == 0) {
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
int total_seconds = (int)(t_total * n_chunk);
if (total_seconds >= 60*60) {
LOG("%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
}
LOG("%.2f minutes\n", total_seconds / 60.0);
}
if (params.compute_ppl) {
const int first = n_ctx/2;
const auto * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
count += n_ctx - first - 1;
LOG("[%d]%.4lf,", i + 1, std::exp(nll / count));
fflush(stdout);
logits.clear();
}
}
LOG("\n");
if (params.compute_ppl) {
nll2 /= count;
nll /= count;
const double ppl = exp(nll);
nll2 -= nll * nll;
if (nll2 > 0) {
nll2 = sqrt(nll2/(count-1));
LOG("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
} else {
LOG("Unexpected negative standard deviation of log(prob)\n");
}
}
return true;
}
int main(int argc, char ** argv) {
common_params params;
params.out_file = "imatrix.dat" ;
params.n_ctx = 512;
params.logits_all = true;
params.escape = false;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
return 1;
}
common_init();
params.n_batch = std::min(params.n_batch, params.n_ctx);
g_collector.set_params(params);
for (const auto & in_file : params.in_files) {
LOG_INF("%s : loading imatrix from '%s'\n", __func__, in_file.c_str());
if (!g_collector.load_imatrix(in_file.c_str())) {
LOG_ERR("%s : failed to load %s\n", __func__, in_file.c_str());
return 1;
}
}
if (params.in_files.size() > 1) {
LOG_INF("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
g_collector.save_imatrix();
}
llama_backend_init();
llama_numa_init(params.numa);
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
params.cb_eval = ik_collect_imatrix;
params.cb_eval_user_data = NULL;
params.warmup = false;
// init
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
if (model == nullptr || ctx == nullptr) {
LOG_ERR("%s : failed to init\n", __func__);
return 1;
}
const int n_ctx_train = llama_model_n_ctx_train(model);
if (params.n_ctx > n_ctx_train) {
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n",
__func__, n_ctx_train, params.n_ctx);
}
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
if (params.prompt.empty()) {
if (params.in_files.empty()) {
LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n");
return 1;
}
LOG_INF("No prompt provided; combining precomputed matrices only.\n");
} else {
if (!compute_imatrix(ctx, params)) {
return 1;
}
}
g_collector.save_imatrix();
LOG("\n");
llama_perf_context_print(ctx);
llama_backend_free();
return 0;
}

5
tools/llama-bench/CMakeLists.txt Normal file
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@ -0,0 +1,5 @@
set(TARGET llama-bench)
add_executable(${TARGET} llama-bench.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

339
tools/llama-bench/README.md Normal file
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@ -0,0 +1,339 @@
# llama.cpp/tools/llama-bench
Performance testing tool for llama.cpp.
## Table of contents
1. [Syntax](#syntax)
2. [Examples](#examples)
1. [Text generation with different models](#text-generation-with-different-models)
2. [Prompt processing with different batch sizes](#prompt-processing-with-different-batch-sizes)
3. [Different numbers of threads](#different-numbers-of-threads)
4. [Different numbers of layers offloaded to the GPU](#different-numbers-of-layers-offloaded-to-the-gpu)
3. [Output formats](#output-formats)
1. [Markdown](#markdown)
2. [CSV](#csv)
3. [JSON](#json)
4. [JSONL](#jsonl)
5. [SQL](#sql)
## Syntax
```
usage: ./llama-bench [options]
options:
-h, --help
-m, --model <filename> (default: models/7B/ggml-model-q4_0.gguf)
-p, --n-prompt <n> (default: 512)
-n, --n-gen <n> (default: 128)
-pg <pp,tg> (default: )
-d, --n-depth <n> (default: 0)
-b, --batch-size <n> (default: 2048)
-ub, --ubatch-size <n> (default: 512)
-ctk, --cache-type-k <t> (default: f16)
-ctv, --cache-type-v <t> (default: f16)
-t, --threads <n> (default: 8)
-C, --cpu-mask <hex,hex> (default: 0x0)
--cpu-strict <0|1> (default: 0)
--poll <0...100> (default: 50)
-ngl, --n-gpu-layers <n> (default: 99)
-rpc, --rpc <rpc_servers> (default: )
-sm, --split-mode <none|layer|row> (default: layer)
-mg, --main-gpu <i> (default: 0)
-nkvo, --no-kv-offload <0|1> (default: 0)
-fa, --flash-attn <0|1> (default: 0)
-mmp, --mmap <0|1> (default: 1)
--numa <distribute|isolate|numactl> (default: disabled)
-embd, --embeddings <0|1> (default: 0)
-ts, --tensor-split <ts0/ts1/..> (default: 0)
-r, --repetitions <n> (default: 5)
--prio <0|1|2|3> (default: 0)
--delay <0...N> (seconds) (default: 0)
-o, --output <csv|json|jsonl|md|sql> (default: md)
-oe, --output-err <csv|json|jsonl|md|sql> (default: none)
-v, --verbose (default: 0)
Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.
```
llama-bench can perform three types of tests:
- Prompt processing (pp): processing a prompt in batches (`-p`)
- Text generation (tg): generating a sequence of tokens (`-n`)
- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`)
With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`).
Each test is repeated the number of times given by `-r`, and the results are averaged. The results are given in average tokens per second (t/s) and standard deviation. Some output formats (e.g. json) also include the individual results of each repetition.
Using the `-d <n>` option, each test can be run at a specified context depth, prefilling the KV cache with `<n>` tokens.
For a description of the other options, see the [main example](../main/README.md).
Note:
- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`.
## Examples
### Text generation with different models
```sh
$ ./llama-bench -m models/7B/ggml-model-q4_0.gguf -m models/13B/ggml-model-q4_0.gguf -p 0 -n 128,256,512
```
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 128 | 132.19 ± 0.55 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 256 | 129.37 ± 0.54 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 512 | 123.83 ± 0.25 |
| llama 13B mostly Q4_0 | 6.86 GiB | 13.02 B | CUDA | 99 | tg 128 | 82.17 ± 0.31 |
| llama 13B mostly Q4_0 | 6.86 GiB | 13.02 B | CUDA | 99 | tg 256 | 80.74 ± 0.23 |
| llama 13B mostly Q4_0 | 6.86 GiB | 13.02 B | CUDA | 99 | tg 512 | 78.08 ± 0.07 |
### Prompt processing with different batch sizes
```sh
$ ./llama-bench -n 0 -p 1024 -b 128,256,512,1024
```
| model | size | params | backend | ngl | n_batch | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------: | ---------- | ---------------: |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 128 | pp 1024 | 1436.51 ± 3.66 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 256 | pp 1024 | 1932.43 ± 23.48 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 512 | pp 1024 | 2254.45 ± 15.59 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | 1024 | pp 1024 | 2498.61 ± 13.58 |
### Different numbers of threads
```sh
$ ./llama-bench -n 0 -n 16 -p 64 -t 1,2,4,8,16,32
```
| model | size | params | backend | threads | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | ---------: | ---------- | ---------------: |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 1 | pp 64 | 6.17 ± 0.07 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 1 | tg 16 | 4.05 ± 0.02 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 2 | pp 64 | 12.31 ± 0.13 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 2 | tg 16 | 7.80 ± 0.07 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 4 | pp 64 | 23.18 ± 0.06 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 4 | tg 16 | 12.22 ± 0.07 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 8 | pp 64 | 32.29 ± 1.21 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 8 | tg 16 | 16.71 ± 0.66 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 16 | pp 64 | 33.52 ± 0.03 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 16 | tg 16 | 15.32 ± 0.05 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 32 | pp 64 | 59.00 ± 1.11 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CPU | 32 | tg 16 | 16.41 ± 0.79 ||
### Different numbers of layers offloaded to the GPU
```sh
$ ./llama-bench -ngl 10,20,30,31,32,33,34,35
```
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 10 | pp 512 | 373.36 ± 2.25 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 10 | tg 128 | 13.45 ± 0.93 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 20 | pp 512 | 472.65 ± 1.25 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 20 | tg 128 | 21.36 ± 1.94 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 30 | pp 512 | 631.87 ± 11.25 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 30 | tg 128 | 40.04 ± 1.82 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 31 | pp 512 | 657.89 ± 5.08 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 31 | tg 128 | 48.19 ± 0.81 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 32 | pp 512 | 688.26 ± 3.29 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 32 | tg 128 | 54.78 ± 0.65 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 33 | pp 512 | 704.27 ± 2.24 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 33 | tg 128 | 60.62 ± 1.76 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 34 | pp 512 | 881.34 ± 5.40 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 34 | tg 128 | 71.76 ± 0.23 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 35 | pp 512 | 2400.01 ± 7.72 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 35 | tg 128 | 131.66 ± 0.49 |
### Different prefilled context
```
$ ./llama-bench -d 0,512
```
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | pp512 | 7340.20 ± 23.45 |
| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | tg128 | 120.60 ± 0.59 |
| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | pp512 @ d512 | 6425.91 ± 18.88 |
| qwen2 7B Q4_K - Medium | 4.36 GiB | 7.62 B | CUDA | 99 | tg128 @ d512 | 116.71 ± 0.60 |
## Output formats
By default, llama-bench outputs the results in markdown format. The results can be output in other formats by using the `-o` option.
### Markdown
```sh
$ ./llama-bench -o md
```
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ---------- | ---------------: |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | pp 512 | 2368.80 ± 93.24 |
| llama 7B mostly Q4_0 | 3.56 GiB | 6.74 B | CUDA | 99 | tg 128 | 131.42 ± 0.59 |
### CSV
```sh
$ ./llama-bench -o csv
```
```csv
build_commit,build_number,cpu_info,gpu_info,backends,model_filename,model_type,model_size,model_n_params,n_batch,n_ubatch,n_threads,cpu_mask,cpu_strict,poll,type_k,type_v,n_gpu_layers,split_mode,main_gpu,no_kv_offload,flash_attn,tensor_split,use_mmap,embeddings,n_prompt,n_gen,n_depth,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts
"8cf427ff","5163","AMD Ryzen 7 7800X3D 8-Core Processor","NVIDIA GeForce RTX 4080","CUDA","models/Qwen2.5-7B-Instruct-Q4_K_M.gguf","qwen2 7B Q4_K - Medium","4677120000","7615616512","2048","512","8","0x0","0","50","f16","f16","99","layer","0","0","0","0.00","1","0","512","0","0","2025-04-24T11:57:09Z","70285660","982040","7285.676949","100.064434"
"8cf427ff","5163","AMD Ryzen 7 7800X3D 8-Core Processor","NVIDIA GeForce RTX 4080","CUDA","models/Qwen2.5-7B-Instruct-Q4_K_M.gguf","qwen2 7B Q4_K - Medium","4677120000","7615616512","2048","512","8","0x0","0","50","f16","f16","99","layer","0","0","0","0.00","1","0","0","128","0","2025-04-24T11:57:10Z","1067431600","3834831","119.915244","0.430617"
```
### JSON
```sh
$ ./llama-bench -o json
```
```json
[
{
"build_commit": "8cf427ff",
"build_number": 5163,
"cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor",
"gpu_info": "NVIDIA GeForce RTX 4080",
"backends": "CUDA",
"model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf",
"model_type": "qwen2 7B Q4_K - Medium",
"model_size": 4677120000,
"model_n_params": 7615616512,
"n_batch": 2048,
"n_ubatch": 512,
"n_threads": 8,
"cpu_mask": "0x0",
"cpu_strict": false,
"poll": 50,
"type_k": "f16",
"type_v": "f16",
"n_gpu_layers": 99,
"split_mode": "layer",
"main_gpu": 0,
"no_kv_offload": false,
"flash_attn": false,
"tensor_split": "0.00",
"use_mmap": true,
"embeddings": false,
"n_prompt": 512,
"n_gen": 0,
"n_depth": 0,
"test_time": "2025-04-24T11:58:50Z",
"avg_ns": 72135640,
"stddev_ns": 1453752,
"avg_ts": 7100.002165,
"stddev_ts": 140.341520,
"samples_ns": [ 74601900, 71632900, 71745200, 71952700, 70745500 ],
"samples_ts": [ 6863.1, 7147.55, 7136.37, 7115.79, 7237.21 ]
},
{
"build_commit": "8cf427ff",
"build_number": 5163,
"cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor",
"gpu_info": "NVIDIA GeForce RTX 4080",
"backends": "CUDA",
"model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf",
"model_type": "qwen2 7B Q4_K - Medium",
"model_size": 4677120000,
"model_n_params": 7615616512,
"n_batch": 2048,
"n_ubatch": 512,
"n_threads": 8,
"cpu_mask": "0x0",
"cpu_strict": false,
"poll": 50,
"type_k": "f16",
"type_v": "f16",
"n_gpu_layers": 99,
"split_mode": "layer",
"main_gpu": 0,
"no_kv_offload": false,
"flash_attn": false,
"tensor_split": "0.00",
"use_mmap": true,
"embeddings": false,
"n_prompt": 0,
"n_gen": 128,
"n_depth": 0,
"test_time": "2025-04-24T11:58:51Z",
"avg_ns": 1076767880,
"stddev_ns": 9449585,
"avg_ts": 118.881588,
"stddev_ts": 1.041811,
"samples_ns": [ 1075361300, 1065089400, 1071761200, 1081934900, 1089692600 ],
"samples_ts": [ 119.03, 120.178, 119.43, 118.307, 117.464 ]
}
]
```
### JSONL
```sh
$ ./llama-bench -o jsonl
```
```json lines
{"build_commit": "8cf427ff", "build_number": 5163, "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", "gpu_info": "NVIDIA GeForce RTX 4080", "backends": "CUDA", "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", "model_type": "qwen2 7B Q4_K - Medium", "model_size": 4677120000, "model_n_params": 7615616512, "n_batch": 2048, "n_ubatch": 512, "n_threads": 8, "cpu_mask": "0x0", "cpu_strict": false, "poll": 50, "type_k": "f16", "type_v": "f16", "n_gpu_layers": 99, "split_mode": "layer", "main_gpu": 0, "no_kv_offload": false, "flash_attn": false, "tensor_split": "0.00", "use_mmap": true, "embeddings": false, "n_prompt": 512, "n_gen": 0, "n_depth": 0, "test_time": "2025-04-24T11:59:33Z", "avg_ns": 70497220, "stddev_ns": 883196, "avg_ts": 7263.609157, "stddev_ts": 90.940578, "samples_ns": [ 71551000, 71222800, 70364100, 69439100, 69909100 ],"samples_ts": [ 7155.74, 7188.71, 7276.44, 7373.37, 7323.8 ]}
{"build_commit": "8cf427ff", "build_number": 5163, "cpu_info": "AMD Ryzen 7 7800X3D 8-Core Processor", "gpu_info": "NVIDIA GeForce RTX 4080", "backends": "CUDA", "model_filename": "models/Qwen2.5-7B-Instruct-Q4_K_M.gguf", "model_type": "qwen2 7B Q4_K - Medium", "model_size": 4677120000, "model_n_params": 7615616512, "n_batch": 2048, "n_ubatch": 512, "n_threads": 8, "cpu_mask": "0x0", "cpu_strict": false, "poll": 50, "type_k": "f16", "type_v": "f16", "n_gpu_layers": 99, "split_mode": "layer", "main_gpu": 0, "no_kv_offload": false, "flash_attn": false, "tensor_split": "0.00", "use_mmap": true, "embeddings": false, "n_prompt": 0, "n_gen": 128, "n_depth": 0, "test_time": "2025-04-24T11:59:33Z", "avg_ns": 1068078400, "stddev_ns": 6279455, "avg_ts": 119.844681, "stddev_ts": 0.699739, "samples_ns": [ 1066331700, 1064864900, 1079042600, 1063328400, 1066824400 ],"samples_ts": [ 120.038, 120.203, 118.624, 120.377, 119.982 ]}
```
### SQL
SQL output is suitable for importing into a SQLite database. The output can be piped into the `sqlite3` command line tool to add the results to a database.
```sh
$ ./llama-bench -o sql
```
```sql
CREATE TABLE IF NOT EXISTS test (
build_commit TEXT,
build_number INTEGER,
cpu_info TEXT,
gpu_info TEXT,
backends TEXT,
model_filename TEXT,
model_type TEXT,
model_size INTEGER,
model_n_params INTEGER,
n_batch INTEGER,
n_ubatch INTEGER,
n_threads INTEGER,
cpu_mask TEXT,
cpu_strict INTEGER,
poll INTEGER,
type_k TEXT,
type_v TEXT,
n_gpu_layers INTEGER,
split_mode TEXT,
main_gpu INTEGER,
no_kv_offload INTEGER,
flash_attn INTEGER,
tensor_split TEXT,
use_mmap INTEGER,
embeddings INTEGER,
n_prompt INTEGER,
n_gen INTEGER,
n_depth INTEGER,
test_time TEXT,
avg_ns INTEGER,
stddev_ns INTEGER,
avg_ts REAL,
stddev_ts REAL
);
INSERT INTO test (build_commit, build_number, cpu_info, gpu_info, backends, model_filename, model_type, model_size, model_n_params, n_batch, n_ubatch, n_threads, cpu_mask, cpu_strict, poll, type_k, type_v, n_gpu_layers, split_mode, main_gpu, no_kv_offload, flash_attn, tensor_split, use_mmap, embeddings, n_prompt, n_gen, n_depth, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('8cf427ff', '5163', 'AMD Ryzen 7 7800X3D 8-Core Processor', 'NVIDIA GeForce RTX 4080', 'CUDA', 'models/Qwen2.5-7B-Instruct-Q4_K_M.gguf', 'qwen2 7B Q4_K - Medium', '4677120000', '7615616512', '2048', '512', '8', '0x0', '0', '50', 'f16', 'f16', '99', 'layer', '0', '0', '0', '0.00', '1', '0', '512', '0', '0', '2025-04-24T12:00:08Z', '69905000', '519516', '7324.546977', '54.032613');
INSERT INTO test (build_commit, build_number, cpu_info, gpu_info, backends, model_filename, model_type, model_size, model_n_params, n_batch, n_ubatch, n_threads, cpu_mask, cpu_strict, poll, type_k, type_v, n_gpu_layers, split_mode, main_gpu, no_kv_offload, flash_attn, tensor_split, use_mmap, embeddings, n_prompt, n_gen, n_depth, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('8cf427ff', '5163', 'AMD Ryzen 7 7800X3D 8-Core Processor', 'NVIDIA GeForce RTX 4080', 'CUDA', 'models/Qwen2.5-7B-Instruct-Q4_K_M.gguf', 'qwen2 7B Q4_K - Medium', '4677120000', '7615616512', '2048', '512', '8', '0x0', '0', '50', 'f16', 'f16', '99', 'layer', '0', '0', '0', '0.00', '1', '0', '0', '128', '0', '2025-04-24T12:00:09Z', '1063608780', '4464130', '120.346696', '0.504647');
```

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# llava (legacy)
add_library(llava OBJECT
llava.cpp
llava.h
clip.cpp
clip.h
)
target_link_libraries(llava PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(llava PUBLIC .)
target_include_directories(llava PUBLIC ../..)
target_include_directories(llava PUBLIC ../../common)
target_compile_features(llava PRIVATE cxx_std_17)
add_library(llava_static STATIC $<TARGET_OBJECTS:llava>)
if (BUILD_SHARED_LIBS)
set_target_properties(llava PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(llava PRIVATE LLAMA_SHARED LLAMA_BUILD)
add_library(llava_shared SHARED $<TARGET_OBJECTS:llava>)
target_link_libraries(llava_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
install(TARGETS llava_shared LIBRARY)
endif()
# mtmd
add_library(mtmd OBJECT
mtmd.cpp
mtmd.h
clip.cpp
clip.h
clip-impl.h
)
target_link_libraries(mtmd PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(mtmd PUBLIC .)
target_include_directories(mtmd PRIVATE ../..)
target_include_directories(mtmd PRIVATE ../../common) # for stb_image.h
target_compile_features(mtmd PRIVATE cxx_std_17)
add_library(mtmd_static STATIC $<TARGET_OBJECTS:mtmd>)
if (BUILD_SHARED_LIBS)
set_target_properties(mtmd PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_compile_definitions(mtmd PRIVATE LLAMA_SHARED LLAMA_BUILD)
add_library(mtmd_shared SHARED $<TARGET_OBJECTS:mtmd>)
target_link_libraries(mtmd_shared PRIVATE ggml llama ${CMAKE_THREAD_LIBS_INIT})
install(TARGETS mtmd_shared LIBRARY)
endif()
if (NOT MSVC)
target_compile_options(llava PRIVATE -Wno-cast-qual) # stb_image.h
target_compile_options(mtmd PRIVATE -Wno-cast-qual) # stb_image.h
endif()
if(TARGET BUILD_INFO)
add_dependencies(llava BUILD_INFO)
add_dependencies(mtmd BUILD_INFO)
endif()
add_executable(llama-llava-cli deprecation-warning.cpp)
add_executable(llama-gemma3-cli deprecation-warning.cpp)
add_executable(llama-minicpmv-cli deprecation-warning.cpp)
add_executable(llama-qwen2vl-cli deprecation-warning.cpp)
set(TARGET llama-mtmd-cli)
add_executable(${TARGET} mtmd-cli.cpp)
set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-mtmd-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common mtmd ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)
set(TARGET llama-llava-clip-quantize-cli)
add_executable(${TARGET} clip-quantize-cli.cpp)
set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-clip-quantize-cli)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# Quantizing CLIP Visual Projector
This is the tool for quantizing the CLIP visual projector model. Quantization reduces the precision of the model's weights, which can significantly decrease the model size and improve inference speed, often with minimal impact on performance.
## Usage
To quantize a CLIP visual projector model, use the following command:
```sh
./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf <type>
```
After the quantization, the visual projector can be used freely with the existing LLAVA cli (LLAVA, Qwen2VL, etc).
### Arguments
- `/path/to/ggml-model-f32.gguf`: The path to the input model file in FP32 or FP16 format.
- `/path/to/ggml-model-quantized.gguf`: The path where the quantized model will be saved.
- `<type>`: The quantization type to apply. This should be an integer corresponding to one of the quantization types defined in the `enum ggml_type`.
### Quantization Types
The following quantization types are supported, based on the `enum ggml_type` definition:
- `2` - `q4_0`: 4-bit quantization with a single scale value.
- `3` - `q4_1`: 4-bit quantization with a separate scale value for each block.
- `6` - `q5_0`: 5-bit quantization with a single scale value.
- `7` - `q5_1`: 5-bit quantization with a separate scale value for each block.
- `8` - `q8_0`: 8-bit quantization with a single scale value.
### Example
To quantize a model using the `q4_0` quantization type, you would run:
```sh
./bin/llama-llava-clip-quantize-cli /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf 2
```
This command will generate a quantized model at `/path/to/ggml-model-quantized.gguf` using the `q4_0` quantization method.
## Notes
- Quantization can lead to a loss in model accuracy, depending on the chosen quantization type. It is recommended to evaluate the quantized model's performance on your specific task to ensure it meets your requirements.
- The quantized model will typically be smaller in size and faster to run, making it more suitable for deployment in resource-constrained environments.

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# Multimodal Support in llama.cpp
This directory provides multimodal capabilities for `llama.cpp`. Initially intended as a showcase for running LLaVA models, its scope has expanded significantly over time to include various other vision-capable models. As a result, LLaVA is no longer the only multimodal architecture supported.
> [!IMPORTANT]
>
> Multimodal support can be viewed as a sub-project within `llama.cpp`. It is under **very heavy development**, and **breaking changes are expected**.
The naming and structure related to multimodal support have evolved, which might cause some confusion. Here's a brief timeline to clarify:
- [#3436](https://github.com/ggml-org/llama.cpp/pull/3436): Initial support for LLaVA 1.5 was added, introducing `llava.cpp` and `clip.cpp`. The `llava-cli` binary was created for model interaction.
- [#4954](https://github.com/ggml-org/llama.cpp/pull/4954): Support for MobileVLM was added, becoming the second vision model supported. This built upon the existing `llava.cpp`, `clip.cpp`, and `llava-cli` infrastructure.
- **Expansion & Fragmentation:** Many new models were subsequently added (e.g., [#7599](https://github.com/ggml-org/llama.cpp/pull/7599), [#10361](https://github.com/ggml-org/llama.cpp/pull/10361), [#12344](https://github.com/ggml-org/llama.cpp/pull/12344), and others). However, `llava-cli` lacked support for the increasingly complex chat templates required by these models. This led to the creation of model-specific binaries like `qwen2vl-cli`, `minicpmv-cli`, and `gemma3-cli`. While functional, this proliferation of command-line tools became confusing for users.
- [#12849](https://github.com/ggml-org/llama.cpp/pull/12849): `libmtmd` was introduced as a replacement for `llava.cpp`. Its goals include providing a single, unified command-line interface, improving the user/developer experience (UX/DX), and supporting both audio and image inputs.
- [#13012](https://github.com/ggml-org/llama.cpp/pull/13012): `mtmd-cli` was added, consolidating the various model-specific CLIs into a single tool powered by `libmtmd`.
## Pre-quantized models
These are ready-to-use models, most of them come with `Q4_K_M` quantization by default:
```sh
# Gemma 3
llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF
llama-mtmd-cli -hf ggml-org/gemma-3-12b-it-GGUF
llama-mtmd-cli -hf ggml-org/gemma-3-27b-it-GGUF
# SmolVLM
llama-mtmd-cli -hf ggml-org/SmolVLM-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/SmolVLM-256M-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/SmolVLM-500M-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF
# Pixtral 12B
llama-mtmd-cli -hf ggml-org/pixtral-12b-GGUF
# Qwen 2 VL
llama-mtmd-cli -hf ggml-org/Qwen2-VL-2B-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/Qwen2-VL-7B-Instruct-GGUF
# Qwen 2.5 VL
llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF
llama-mtmd-cli -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF
# Mistral Small 3.1 24B (IQ2_M quantization)
llama-mtmd-cli -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF --chat-template mistral-v7
```
## How it works and what is `mmproj`?
Multimodal support in `llama.cpp` works by encoding images into embeddings using a separate model component, and then feeding these embeddings into the language model.
This approach keeps the multimodal components distinct from the core `libllama` library. Separating these allows for faster, independent development cycles. While many modern vision models are based on Vision Transformers (ViTs), their specific pre-processing and projection steps can vary significantly. Integrating this diverse complexity directly into `libllama` is currently challenging.
Consequently, running a multimodal model typically requires two GGUF files:
1. The standard language model file.
2. A corresponding **multimodal projector (`mmproj`)** file, which handles the image encoding and projection.
## What is `libmtmd`?
As outlined in the history, `libmtmd` is the modern library designed to replace the original `llava.cpp` implementation for handling multimodal inputs.
Built upon `clip.cpp` (similar to `llava.cpp`), `libmtmd` offers several advantages:
- **Unified Interface:** Aims to consolidate interaction for various multimodal models.
- **Improved UX/DX:** Features a more intuitive API, inspired by the `Processor` class in the Hugging Face `transformers` library.
- **Flexibility:** Designed to support multiple input types (text, audio, images) while respecting the wide variety of chat templates used by different models.
## How to obtain `mmproj`
Multimodal projector (`mmproj`) files are specific to each model architecture.
For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` flag to get the `mmproj` file:
- [Gemma 3](https://huggingface.co/collections/google/gemma-3-release-67c6c6f89c4f76621268bb6d) - Note: 1B variant does not have vision support
- SmolVLM (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
- SmolVLM2 (from [HuggingFaceTB](https://huggingface.co/HuggingFaceTB))
- [Pixtral 12B](https://huggingface.co/mistral-community/pixtral-12b) - only works with `transformers`-compatible checkpoint
- Qwen 2 VL and Qwen 2.5 VL (from [Qwen](https://huggingface.co/Qwen))
- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)
For older models, please refer to the relevant guide for instructions on how to obtain or create them:
- [LLaVA](../../docs/multimodal/llava.md)
- [MobileVLM](../../docs/multimodal/MobileVLM.md)
- [GLM-Edge](../../docs/multimodal/glmedge.md)
- [MiniCPM-V 2.5](../../docs/multimodal/minicpmv2.5.md)
- [MiniCPM-V 2.6](../../docs/multimodal/minicpmv2.6.md)
- [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md)
- [IBM Granite Vision](../../docs/multimodal/granitevision.md)
- [Google Gemma 3](../../docs/multimodal/gemma3.md)

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#!/bin/bash
model_dir="/Users/cxt/model/llm/mobileVLM/MobileVLM-1.7B_processed"
projector_name="mmproj-model-f16.gguf"
llama_name="ggml-model-q4_k.gguf"
img_dir="/Users/cxt/model/llm"
img_name="demo.jpg"
prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:"
# img_name="cat.jpeg"
# prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhat is in the image? ASSISTANT:"
program_dir="build_64/bin"
binName="llama-mtmd-cli"
n_threads=4
deviceDir="/data/local/tmp"
saveDir="output"
if [ ! -d ${saveDir} ]; then
mkdir ${saveDir}
fi
function android_run() {
# # copy resource into device
# adb push ${model_dir}/${projector_name} ${deviceDir}/${projector_name}
# adb push ${model_dir}/${llama_name} ${deviceDir}/${llama_name}
adb push ${img_dir}/${img_name} ${deviceDir}/${img_name}
# copy program into device
adb push ${program_dir}/${binName} ${deviceDir}/${binName}
adb shell "chmod 0777 ${deviceDir}/${binName}"
# run
adb shell "echo cd ${deviceDir} ${deviceDir}/${binName} \
-m ${deviceDir}/${llama_name} \
--mmproj ${deviceDir}/${projector_name} \
-t ${n_threads} \
--image ${deviceDir}/${img_name} \
-p \"${prompt}\" \
> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt"
adb shell "cd ${deviceDir}; pwd; ${deviceDir}/${binName} \
-m ${deviceDir}/${llama_name} \
--mmproj ${deviceDir}/${projector_name} \
-t ${n_threads} \
--image ${deviceDir}/${img_name} \
-p \"${prompt}\" \
>> ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt 2>&1"
adb pull ${deviceDir}/${modelName}_${projector_name}_${n_threads}_${img_name}.txt ${saveDir}
}
android_run
echo "android_run is Done!"

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#!/bin/bash
cmake ../../../../ \
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
-DCMAKE_BUILD_TYPE=Release \
-DANDROID_ABI="arm64-v8a" \
-DANDROID_PLATFORM=android-23 $1
make -j4

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#include "ggml.h"
#include "gguf.h"
#include "clip.h"
#include <climits>
#include <cstdarg>
#include <string>
#include <map>
#include <sstream>
#include <vector>
#include <memory>
// Internal header for clip.cpp
#define KEY_FTYPE "general.file_type"
#define KEY_NAME "general.name"
#define KEY_DESCRIPTION "general.description"
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
#define KEY_USE_GELU "clip.use_gelu"
#define KEY_USE_SILU "clip.use_silu"
#define KEY_N_EMBD "clip.vision.embedding_length"
#define KEY_N_FF "clip.vision.feed_forward_length"
#define KEY_N_BLOCK "clip.vision.block_count"
#define KEY_N_HEAD "clip.vision.attention.head_count"
#define KEY_LAYER_NORM_EPS "clip.vision.attention.layer_norm_epsilon"
#define KEY_PROJ_DIM "clip.vision.projection_dim"
#define KEY_IMAGE_SIZE "clip.vision.image_size"
#define KEY_PATCH_SIZE "clip.vision.patch_size"
#define KEY_IMAGE_MEAN "clip.vision.image_mean"
#define KEY_IMAGE_STD "clip.vision.image_std"
#define KEY_FEATURE_LAYER "clip.vision.feature_layer"
#define KEY_PROJ_SCALE_FACTOR "clip.vision.projector.scale_factor"
#define KEY_PROJ_TYPE "clip.projector_type"
#define KEY_SPATIAL_MERGE_SIZE "clip.vision.spatial_merge_size"
#define KEY_USE_GLU_MLP "clip.use_glu_mlp" // for qwen2.5vl
#define KEY_USE_RMS_NORM "clip.use_rms_norm" // for qwen2.5vl
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
#define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern"
#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size"
//
// tensor name constants
//
#define TN_POS_EMBD "%s.position_embd.weight"
#define TN_CLASS_EMBD "v.class_embd"
#define TN_PATCH_EMBD "v.patch_embd.weight" // not rename tensor with ".0" postfix for backwrad compat
#define TN_PATCH_EMBD_1 "v.patch_embd.weight.1"
#define TN_PATCH_BIAS "v.patch_embd.bias"
#define TN_ATTN_K "%s.blk.%d.attn_k.%s"
#define TN_ATTN_Q "%s.blk.%d.attn_q.%s"
#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s"
#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s"
#define TN_LN_1 "%s.blk.%d.ln1.%s"
#define TN_LN_2 "%s.blk.%d.ln2.%s"
#define TN_LN_PRE "%s.pre_ln.%s"
#define TN_LN_POST "%s.post_ln.%s"
#define TN_LLAVA_PROJ "mm.%d.%s"
#define TN_MVLM_PROJ_MLP "mm.model.mlp.%d.%s"
#define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
#define TN_MVLM_PROJ_PEG "mm.model.peg.%d.%s"
#define TN_IMAGE_NEWLINE "model.image_newline"
#define TN_MM_INP_NORM "mm.input_norm.weight"
#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3
#define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3
#define TN_MM_PROJECTOR "mm.model.fc.weight" // idefics3
#define TN_MM_PATCH_MERGER "mm.patch_merger.weight" // mistral small 3.1
#define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral
// mimicpmv
#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
#define TN_MINICPMV_QUERY "resampler.query"
#define TN_MINICPMV_PROJ "resampler.proj.weight"
#define TN_MINICPMV_KV_PROJ "resampler.kv.weight"
#define TN_MINICPMV_ATTN "resampler.attn.%s.%s"
#define TN_MINICPMV_LN "resampler.ln_%s.%s"
#define TN_GLM_ADAPER_CONV "adapter.conv.%s"
#define TN_GLM_ADAPTER_LINEAR "adapter.linear.linear.%s"
#define TN_GLM_ADAPTER_NORM_1 "adapter.linear.norm1.%s"
#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
enum projector_type {
PROJECTOR_TYPE_MLP,
PROJECTOR_TYPE_MLP_NORM,
PROJECTOR_TYPE_LDP,
PROJECTOR_TYPE_LDPV2,
PROJECTOR_TYPE_MINICPMV,
PROJECTOR_TYPE_GLM_EDGE,
PROJECTOR_TYPE_QWEN2VL,
PROJECTOR_TYPE_GEMMA3,
PROJECTOR_TYPE_IDEFICS3,
PROJECTOR_TYPE_PIXTRAL,
PROJECTOR_TYPE_QWEN25VL,
PROJECTOR_TYPE_UNKNOWN,
};
static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_MLP, "mlp" },
{ PROJECTOR_TYPE_LDP, "ldp" },
{ PROJECTOR_TYPE_LDPV2, "ldpv2"},
{ PROJECTOR_TYPE_MINICPMV, "resampler"},
{ PROJECTOR_TYPE_GLM_EDGE, "adapter"},
{ PROJECTOR_TYPE_QWEN2VL, "qwen2vl_merger"},
{ PROJECTOR_TYPE_QWEN25VL, "qwen2.5vl_merger"},
{ PROJECTOR_TYPE_GEMMA3, "gemma3"},
{ PROJECTOR_TYPE_IDEFICS3, "idefics3"},
{ PROJECTOR_TYPE_PIXTRAL, "pixtral"},
};
static projector_type clip_projector_type_from_string(const std::string & str) {
for (const auto & pair : PROJECTOR_TYPE_NAMES) {
if (pair.second == str) {
return pair.first;
}
}
return PROJECTOR_TYPE_UNKNOWN;
}
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
std::vector<uint8_t> buf;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
std::vector<float> buf;
};
//
// logging
//
static void clip_log_callback_default(enum ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
fputs(text, stderr);
fflush(stderr);
}
struct clip_logger_state {
ggml_log_level verbosity_thold;
ggml_log_callback log_callback;
void * log_callback_user_data;
};
extern struct clip_logger_state g_logger_state;
static void clip_log_internal_v(enum ggml_log_level level, const char * format, va_list args) {
if (format == NULL) {
return;
}
va_list args_copy;
va_copy(args_copy, args);
char buffer[128];
int len = vsnprintf(buffer, 128, format, args);
if (len < 128) {
g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data);
} else {
char * buffer2 = (char *) calloc(len + 1, sizeof(char));
vsnprintf(buffer2, len + 1, format, args_copy);
buffer2[len] = 0;
g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data);
free(buffer2);
}
va_end(args_copy);
}
static void clip_log_internal(enum ggml_log_level level, const char * format, ...) {
va_list args;
va_start(args, format);
clip_log_internal_v(level, format, args);
va_end(args);
}
#define LOG_TMPL(level, ...) \
do { \
if ((level) >= g_logger_state.verbosity_thold) { \
clip_log_internal((level), __VA_ARGS__); \
} \
} while (0)
#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, __VA_ARGS__)
#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, __VA_ARGS__)
#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, __VA_ARGS__)
//
// cpp wrappers
//
// wrapper for clip_image_size
struct clip_image_size_deleter {
void operator()(clip_image_size * val) { clip_image_size_free(val); }
};
typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
// wrapper for clip_image_u8
struct clip_image_u8_deleter {
void operator()(clip_image_u8 * val) { clip_image_u8_free(val); }
};
typedef std::unique_ptr<clip_image_u8, clip_image_u8_deleter> clip_image_u8_ptr;
// wrapper for clip_image_f32
struct clip_image_f32_deleter {
void operator()(clip_image_f32 * val) { clip_image_f32_free(val); }
};
typedef std::unique_ptr<clip_image_f32, clip_image_f32_deleter> clip_image_f32_ptr;
struct clip_image_u8_batch {
std::vector<clip_image_u8_ptr> entries;
};
struct clip_image_f32_batch {
std::vector<clip_image_f32_ptr> entries;
};
//
// common utils
//
static std::string string_format(const char * fmt, ...) {
va_list ap;
va_list ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
GGML_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), buf.size());
}
static void string_replace_all(std::string & s, const std::string & search, const std::string & replace) {
if (search.empty()) {
return;
}
std::string builder;
builder.reserve(s.length());
size_t pos = 0;
size_t last_pos = 0;
while ((pos = s.find(search, last_pos)) != std::string::npos) {
builder.append(s, last_pos, pos - last_pos);
builder.append(replace);
last_pos = pos + search.length();
}
builder.append(s, last_pos, std::string::npos);
s = std::move(builder);
}
// split string by a `std::string delim` instead of `char delim`
static std::vector<std::string> string_split_str(std::string s, const std::string & delimiter) {
std::vector<std::string> tokens;
size_t pos = 0;
std::string token;
while ((pos = s.find(delimiter)) != std::string::npos) {
token = s.substr(0, pos);
tokens.push_back(token);
s.erase(0, pos + delimiter.length());
}
tokens.push_back(s);
return tokens;
}
//
// gguf utils
//
static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
switch (type) {
case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]);
case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]);
case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]);
case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]);
case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]);
case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]);
case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]);
case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]);
case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]);
case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]);
case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false";
default: return string_format("unknown type %d", type);
}
}
static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
switch (type) {
case GGUF_TYPE_STRING:
return gguf_get_val_str(ctx_gguf, i);
case GGUF_TYPE_ARRAY:
{
const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
int arr_n = gguf_get_arr_n(ctx_gguf, i);
const void * data = arr_type == GGUF_TYPE_STRING ? nullptr : gguf_get_arr_data(ctx_gguf, i);
std::stringstream ss;
ss << "[";
for (int j = 0; j < arr_n; j++) {
if (arr_type == GGUF_TYPE_STRING) {
std::string val = gguf_get_arr_str(ctx_gguf, i, j);
// escape quotes
string_replace_all(val, "\\", "\\\\");
string_replace_all(val, "\"", "\\\"");
ss << '"' << val << '"';
} else if (arr_type == GGUF_TYPE_ARRAY) {
ss << "???";
} else {
ss << gguf_data_to_str(arr_type, data, j);
}
if (j < arr_n - 1) {
ss << ", ";
}
}
ss << "]";
return ss.str();
}
default:
return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
}
}
//
// API used internally with mtmd
//
projector_type clip_get_projector_type(const struct clip_ctx * ctx);

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#include "arg.h"
#include "base64.hpp"
#include "log.h"
#include "common.h"
#include "sampling.h"
#include "clip.h"
#include "llava.h"
#include "llama.h"
#include "ggml.h"
static void print_usage(int argc, char ** argv) {
(void) argc;
fprintf(stderr, "usage: %s /path/to/ggml-model-f32.gguf /path/to/ggml-model-quantized.gguf type\n", argv[0]);
fprintf(stderr, " type = 2 - q4_0\n");
fprintf(stderr, " type = 3 - q4_1\n");
fprintf(stderr, " type = 6 - q5_0\n");
fprintf(stderr, " type = 7 - q5_1\n");
fprintf(stderr, " type = 8 - q8_0\n");
}
int main(int argc, char ** argv) {
if (argc != 4) {
print_usage(argc, argv);
return 1;
}
const std::string fname_inp = argv[1];
const std::string fname_out = argv[2];
const int itype = atoi(argv[3]);
const int64_t t_main_start_us = ggml_time_us();
int64_t t_quantize_us = 0;
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!clip_model_quantize(fname_inp.c_str(), fname_out.c_str(), itype)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}
t_quantize_us = ggml_time_us() - t_start_us;
}
// report timing
{
const int64_t t_main_end_us = ggml_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us / 1000.0f);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us) / 1000.0f);
}
return 0;
}

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#ifndef CLIP_H
#define CLIP_H
#include "ggml.h"
#include <stddef.h>
#include <stdint.h>
#ifdef LLAMA_SHARED
# if defined(_WIN32) && !defined(__MINGW32__)
# ifdef LLAMA_BUILD
# define CLIP_API __declspec(dllexport)
# else
# define CLIP_API __declspec(dllimport)
# endif
# else
# define CLIP_API __attribute__ ((visibility ("default")))
# endif
#else
# define CLIP_API
#endif
#ifdef __cplusplus
extern "C" {
#endif
struct clip_ctx;
struct clip_image_size {
int width;
int height;
};
struct clip_image_f32;
struct clip_image_u8_batch;
struct clip_image_f32_batch;
struct clip_context_params {
bool use_gpu;
enum ggml_log_level verbosity;
};
// deprecated, use clip_init
CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);
CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);
CLIP_API void clip_free(struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h);
CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx);
CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx);
CLIP_API int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
// TODO: should be enum, not string
CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
GGML_DEPRECATED(CLIP_API int clip_n_patches(const struct clip_ctx * ctx),
"use clip_n_output_tokens instead");
GGML_DEPRECATED(CLIP_API int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img),
"use clip_n_output_tokens instead");
CLIP_API int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
// for M-RoPE, this will be the number of token positions in X and Y directions
// for other models, X will be the total number of tokens and Y will be 1
CLIP_API int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
CLIP_API int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
// this should be equal to the embedding dimension of the text model
CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
CLIP_API struct clip_image_size * clip_get_load_image_size(struct clip_ctx * ctx_clip);
CLIP_API struct clip_image_size * clip_image_size_init();
CLIP_API struct clip_image_u8 * clip_image_u8_init ();
CLIP_API struct clip_image_f32 * clip_image_f32_init();
CLIP_API struct clip_image_f32_batch * clip_image_f32_batch_init(); // only used by libllava
// nx, ny are the output image dimensions
CLIP_API unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
CLIP_API void clip_image_size_free (struct clip_image_size * img_size);
CLIP_API void clip_image_u8_free (struct clip_image_u8 * img);
CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch * batch);
CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
// use for accessing underlay data of clip_image_f32_batch
CLIP_API size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
CLIP_API size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
CLIP_API size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
CLIP_API struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
/**
* Build image from pixels decoded by other libraries instead of stb_image.h for better performance.
* The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes
*/
CLIP_API void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
/** interpret bytes as an image file with length bytes_length, and use the result to populate img */
CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
CLIP_API bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
CLIP_API bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
CLIP_API bool clip_model_quantize(const char * fname_inp, const char * fname_out, int itype);
CLIP_API int clip_is_minicpmv(const struct clip_ctx * ctx);
CLIP_API bool clip_is_glm(const struct clip_ctx * ctx);
CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
CLIP_API bool clip_is_llava(const struct clip_ctx * ctx);
CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx);
CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
#ifdef __cplusplus
}
#endif
#endif // CLIP_H

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import argparse
import os
import json
import re
import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel, SiglipVisionModel
TEXT = "clip.text"
VISION = "clip.vision"
def k(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch)
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
if name in (
"logit_scale",
"text_model.embeddings.position_ids",
"vision_model.embeddings.position_ids",
):
return True
if has_llava and name in ["visual_projection.weight", "vision_model.post_layernorm.weight", "vision_model.post_layernorm.bias"]:
return True
if name.startswith("v") and not has_vision:
return True
if name.startswith("t") and not has_text:
return True
return False
def get_tensor_name(name: str) -> str:
# Standardize the transformers llava next keys for
# image newline / mm projector with the classes in haotian-liu LLaVA
if name == "image_newline":
return "model.image_newline"
if name.startswith("multi_modal_projector"):
name = name.replace("multi_modal_projector", "mm")
if "linear_1" in name:
name = name.replace("linear_1", "0")
if "linear_2" in name:
name = name.replace("linear_2", "2")
return name
if "projection" in name:
return name
if "mm_projector" in name:
name = name.replace("model.mm_projector", "mm")
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
return name
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument('--bigendian', action="store_true", default=False, help="Model is executed on big-endian machine")
ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
# Selectable visual encoders that are compatible with this script
encoder_group = ap.add_mutually_exclusive_group()
encoder_group.add_argument("--clip-model-is-openclip", action="store_true", required=False,
help="The clip model is from openclip (for ViT-SO400M type))")
encoder_group.add_argument("--clip-model-is-siglip", action="store_true", required=False,
help="the visual encoder is Siglip.")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
if args.text_only and args.vision_only:
print("--text-only and --image-only arguments cannot be specified at the same time.")
exit(1)
if args.use_f32:
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if (
args.clip_model_is_vision or
not os.path.exists(dir_model + "/vocab.json") or
args.clip_model_is_openclip or
args.clip_model_is_siglip
):
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = config["text_config"]
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if args.use_f32:
ftype = 0
if args.clip_model_is_siglip:
model = SiglipVisionModel.from_pretrained(dir_model)
processor = None
elif args.clip_model_is_vision or args.clip_model_is_openclip:
model = CLIPVisionModel.from_pretrained(dir_model)
processor = None
else:
model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)
fname_middle = None
has_text_encoder = True
has_vision_encoder = True
has_llava_projector = False
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_llava_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip", endianess=GGUFEndian.LITTLE if not args.bigendian else GGUFEndian.BIG)
fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_llava_projector", has_llava_projector)
fout.add_file_type(ftype)
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
fout.add_name(model_name)
if args.text_only:
fout.add_description("text-only CLIP model")
elif args.vision_only and not has_llava_projector:
fout.add_description("vision-only CLIP model")
elif has_llava_projector:
fout.add_description("image encoder for LLaVA")
# add projector type
fout.add_string("clip.projector_type", args.projector_type)
else:
fout.add_description("two-tower CLIP model")
if has_text_encoder:
assert t_hparams is not None
assert tokens is not None
if args.clip_model_is_siglip:
text_projection_dim = 0
else:
text_projection_dim = t_hparams.get("projection_dim", config["projection_dim"])
# text_model hparams
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
fout.add_uint32("clip.text.projection_dim", text_projection_dim)
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
fout.add_token_list(tokens)
def get_non_negative_vision_feature_layers(v_hparams):
"""
Determine the vision feature layer(s) for the llava model, which are indices into the
hidden states of the visual encoder. Note that the hidden states array generally takes the
form:
[<emb input>, <output of enc block 0>, ... <output of enc block num_hidden_layers>]
so feature indices should be offset as n+1 to get the output of encoder block n.
We convert all vision feature layers to non-negative so that -1 can be used in
the model as an unset value. If no vision feature layer is found, we leave it unset.
"""
num_hidden_layers = v_hparams["num_hidden_layers"]
to_non_negative = lambda layer_idx: layer_idx if layer_idx >= 0 else num_hidden_layers + layer_idx + 1
feature_layers_key = None
# Key used for llava models in transformers
if "vision_feature_layer" in config:
feature_layers_key = "vision_feature_layer"
# Key used for llava models in the original format
elif "mm_vision_select_layer" in config:
feature_layers_key = "mm_vision_select_layer"
if feature_layers_key is not None:
feature_layers = config[feature_layers_key]
if isinstance(feature_layers, int):
feature_layers = [feature_layers]
return [to_non_negative(feature_layer) for feature_layer in feature_layers]
# Determine if we have explicitly specified vision feature layers in our config
feature_layers = get_non_negative_vision_feature_layers(v_hparams)
if has_vision_encoder:
# Siglip does not have a visual projector; set projection dim to 0
if args.clip_model_is_siglip:
visual_projection_dim = 0
else:
visual_projection_dim = v_hparams.get("projection_dim", config["projection_dim"])
# set vision_model hparams
fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
fout.add_uint32("clip.vision.projection_dim", visual_projection_dim)
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
if feature_layers:
block_count = max(feature_layers)
else:
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
# /**
# "image_grid_pinpoints": [
# [
# 336,
# 672
# ],
# [
# 672,
# 336
# ],
# [
# 672,
# 672
# ],
# [
# 1008,
# 336
# ],
# [
# 336,
# 1008
# ]
# ],
# Flattened:
# [
# 336, 672,
# 672, 336,
# 672, 672,
# 1008, 336,
# 336, 1008
# ]
# *
# */
if "image_grid_pinpoints" in v_hparams:
# flatten it
image_grid_pinpoints = []
for pinpoint in v_hparams["image_grid_pinpoints"]:
for p in pinpoint:
image_grid_pinpoints.append(p)
fout.add_array("clip.vision.image_grid_pinpoints", image_grid_pinpoints)
if "image_crop_resolution" in v_hparams:
fout.add_uint32("clip.vision.image_crop_resolution", v_hparams["image_crop_resolution"])
if "image_aspect_ratio" in v_hparams:
fout.add_string("clip.vision.image_aspect_ratio", v_hparams["image_aspect_ratio"])
if "image_split_resolution" in v_hparams:
fout.add_uint32("clip.vision.image_split_resolution", v_hparams["image_split_resolution"])
if "mm_patch_merge_type" in v_hparams:
fout.add_string("clip.vision.mm_patch_merge_type", v_hparams["mm_patch_merge_type"])
if "mm_projector_type" in v_hparams:
fout.add_string("clip.vision.mm_projector_type", v_hparams["mm_projector_type"])
if feature_layers:
fout.add_array("clip.vision.feature_layer", feature_layers)
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean # pyright: ignore[reportAttributeAccessIssue]
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std # pyright: ignore[reportAttributeAccessIssue]
else:
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)
use_gelu = v_hparams["hidden_act"] == "gelu"
fout.add_bool("clip.use_gelu", use_gelu)
if has_llava_projector:
# By default, we drop the last layer for llava projector
# models unless we have explicitly set vision feature layers
if feature_layers is None:
model.vision_model.encoder.layers.pop(-1)
else:
model.vision_model.encoder.layers = model.vision_model.encoder.layers[:max(feature_layers)]
projector = torch.load(args.llava_projector)
for name, data in projector.items():
name = get_tensor_name(name)
# pw and dw conv ndim==4
if data.ndim == 2 or data.ndim == 4:
data = data.squeeze().numpy().astype(np.float16)
else:
data = data.squeeze().numpy().astype(np.float32)
fout.add_tensor(name, data)
print("Projector tensors added\n")
state_dict = model.state_dict()
for name, data in state_dict.items():
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
# we don't need this
print(f"skipping parameter: {name}")
continue
name = get_tensor_name(name)
data = data.squeeze().numpy()
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
ftype_cur = 0
if n_dims == 4:
print(f"tensor {name} is always saved in f16")
data = data.astype(np.float16)
ftype_cur = 1
elif ftype == 1:
if name[-7:] == ".weight" and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
fout.add_tensor(name, data)
fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()
print("Done. Output file: " + fname_out)

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tools/llava/deprecation-warning.cpp Normal file
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#include <cstdio>
#include <string>
int main(int argc, char** argv) {
std::string filename = "main";
if (argc >= 1) {
filename = argv[0];
}
// Get only the program name from the full path
size_t pos = filename.find_last_of("/\\");
if (pos != std::string::npos) {
filename = filename.substr(pos+1);
}
fprintf(stdout, "\n");
fprintf(stdout, "WARNING: The binary '%s' is deprecated.\n", filename.c_str());
fprintf(stdout, "Please use 'llama-mtmd-cli' instead.\n");
fprintf(stdout, "\n");
return EXIT_FAILURE;
}

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tools/llava/glmedge-convert-image-encoder-to-gguf.py Normal file
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import argparse
import os
import json
import re
import torch
import numpy as np
from gguf import *
TEXT = "clip.text"
VISION = "clip.vision"
from transformers import SiglipVisionModel, SiglipVisionConfig
def k(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch)
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
if name in (
"logit_scale",
"text_model.embeddings.position_ids",
"vision_model.embeddings.position_ids",
):
return True
if name in (
"vision_model.head.probe",
"vision_model.head.attention.in_proj_weight",
"vision_model.head.attention.in_proj_bias",
"vision_model.head.attention.out_proj.weight",
"vision_model.head.attention.out_proj.bias",
"vision_model.head.layernorm.weight",
"vision_model.head.layernorm.bias",
"vision_model.head.mlp.fc1.weight",
"vision_model.head.mlp.fc1.bias",
"vision_model.head.mlp.fc2.weight",
"vision_model.head.mlp.fc2.bias"
):
return True
if name.startswith("v") and not has_vision:
return True
if name.startswith("t") and not has_text:
return True
return False
def get_tensor_name(name: str) -> str:
if "projection" in name:
return name
if "mm_projector" in name:
name = name.replace("model.mm_projector", "mm")
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
return name
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
help="The clip model is from openclip (for ViT-SO400M type))")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2","adapter"], default="adapter")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
default_image_mean = [0.5, 0.5, 0.5]
default_image_std = [0.5, 0.5, 0.5]
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
if args.text_only and args.vision_only:
print("--text-only and --image-only arguments cannot be specified at the same time.")
exit(1)
if args.use_f32:
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = None
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if args.use_f32:
ftype = 0
vision_config = SiglipVisionConfig(**v_hparams)
model = SiglipVisionModel(vision_config)
model.load_state_dict(torch.load(os.path.join(dir_model, "glm.clip")))
fname_middle = None
has_text_encoder = False
has_vision_encoder = True
has_glm_projector = True
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_glm_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")
fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_glm_projector", has_glm_projector)
fout.add_file_type(ftype)
model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
fout.add_name(model_name)
if has_glm_projector:
fout.add_description("image encoder for glm4v")
fout.add_string("clip.projector_type", "adapter")
else:
fout.add_description("two-tower CLIP model")
if has_text_encoder:
assert t_hparams is not None
assert tokens is not None
# text_model hparams
fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
fout.add_token_list(tokens)
if has_vision_encoder:
# vision_model hparams
fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
fout.add_uint32("clip.vision.projection_dim", 0)
fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), v_hparams["num_hidden_layers"])
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)
fout.add_bool("clip.use_gelu", True)
if has_glm_projector:
# model.vision_model.encoder.layers.pop(-1) # pyright: ignore[reportAttributeAccessIssue]
projector = torch.load(args.llava_projector)
for name, data in projector.items():
name = get_tensor_name(name)
# pw and dw conv ndim==4
if data.ndim == 2 or data.ndim == 4:
data = data.squeeze().numpy().astype(np.float16)
else:
data = data.squeeze().numpy().astype(np.float32)
if name.startswith("vision."):
name=name.replace("vision.","")
fout.add_tensor(name, data)
print(f"Projector {name} - {data.dtype} - shape = {data.shape}")
# print(f"Projector {name} tensors added\n")
state_dict = model.state_dict() # pyright: ignore[reportAttributeAccessIssue]
for name, data in state_dict.items():
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_glm_projector):
# we don't need this
print(f"skipping parameter: {name}")
continue
name = get_tensor_name(name)
data = data.squeeze().numpy()
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
ftype_cur = 0
if n_dims == 4:
print(f"tensor {name} is always saved in f16")
data = data.astype(np.float16)
ftype_cur = 1
elif ftype == 1:
if name[-7:] == ".weight" and n_dims == 2:
# print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
# print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
# print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
print(f"siglip {name} - {data.dtype} - shape = {data.shape}")
# print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
fout.add_tensor(name, data)
fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()
print("Done. Output file: " + fname_out)

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import argparse
import os
import torch
from transformers import AutoModel
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to GLM model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True)
checkpoint = model.state_dict()
# get a list of mm tensor names
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.adapter.")]
# store these tensors in a new dictionary and torch.save them
projector = {name: checkpoint[name].float() for name in mm_tensors}
torch.save(projector, f"{args.model}/glm.projector")
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vision.vit.model.vision_model.")]
if len(clip_tensors) > 0:
clip = {name.replace("vision.vit.model.", ""): checkpoint[name].float() for name in clip_tensors}
torch.save(clip, f"{args.model}/glm.clip")
# added tokens should be removed to be able to convert Mistral models
if os.path.exists(f"{args.model}/added_tokens.json"):
with open(f"{args.model}/added_tokens.json", "w") as f:
f.write("{}\n")
print("Done!")
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
print(f"Also, use {args.model}glm.projector to prepare a glm-encoder.gguf file.")

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#include "clip.h"
#include "llava.h"
#include "llama.h"
#include <algorithm>
#include <cerrno>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <vector>
#include <memory>
#if defined(LLAVA_LOG_OFF)
# define LOG_INF(...)
# define LOG_WRN(...)
# define LOG_ERR(...)
# define LOG_DBG(...)
#else // defined(LLAVA_LOG_OFF)
# define LOG_INF(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
# define LOG_WRN(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
# define LOG_ERR(...) do { fprintf(stderr, __VA_ARGS__); } while (0)
# define LOG_DBG(...) do { fprintf(stdout, __VA_ARGS__); } while (0)
#endif // defined(LLAVA_LOG_OFF)
// RGB uint8 image
struct clip_image_u8 {
int nx;
int ny;
std::vector<uint8_t> buf;
};
// RGB float32 image (NHWC)
// Memory layout: RGBRGBRGB...
struct clip_image_f32 {
int nx;
int ny;
std::vector<float> buf;
};
struct clip_image_grid_shape {
int first;
int second;
};
// convenience cpp wrapper
struct clip_image_f32_batch_deleter {
void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
};
typedef std::unique_ptr<clip_image_f32_batch, clip_image_f32_batch_deleter> clip_image_f32_batch_ptr;
struct clip_image_size_deleter {
void operator()(clip_image_f32_batch * val) { clip_image_f32_batch_free(val); }
};
typedef std::unique_ptr<clip_image_size, clip_image_size_deleter> clip_image_size_ptr;
/**
* Selects the best resolution from a list of possible resolutions based on the original size.
*
* @param original_size The original size of the image in the format (width, height).
* @param possible_resolutions A list of possible resolutions in the format [(width1, height1), (width2, height2), ...].
* @return The best fit resolution in the format (width, height).
*/
static std::pair<int, int> select_best_resolution(const std::pair<int, int>& original_size, const std::vector<std::pair<int, int>>& possible_resolutions) {
int original_width = original_size.first;
int original_height = original_size.second;
std::pair<int, int> best_fit;
int max_effective_resolution = 0;
int min_wasted_resolution = std::numeric_limits<int>::max();
for (const auto& resolution : possible_resolutions) {
int width = resolution.first;
int height = resolution.second;
float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
int downscaled_width = static_cast<int>(original_width * scale);
int downscaled_height = static_cast<int>(original_height * scale);
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
int wasted_resolution = (width * height) - effective_resolution;
// LOG_DBG("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
max_effective_resolution = effective_resolution;
min_wasted_resolution = wasted_resolution;
best_fit = resolution;
}
}
return best_fit;
}
/**
* @brief Get the anyres image grid shape object
*
* @param image_size
* @param grid_pinpoints
* @param image_patch_size
* @return <int, int>
*/
static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<int, int> & image_size, const std::vector<std::pair<int, int>> & grid_pinpoints, int image_patch_size) {
/**
Conversion from gguf flat array to vector:
std::vector<std::pair<int, int>> possible_resolutions;
for (int i = 0; i < 32 && params.image_grid_pinpoints[i] != 0; i+=2) {
possible_resolutions.push_back({params.image_grid_pinpoints[i], params.image_grid_pinpoints[i+1]});
}
*/
auto best_resolution = select_best_resolution(image_size, grid_pinpoints);
return {best_resolution.first / image_patch_size, best_resolution.second / image_patch_size};
}
// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out, clip_image_f32 * img_input) {
struct {
struct ggml_context * ctx;
} model;
const int32_t image_size = clip_get_image_size(ctx_clip);
const int32_t patch_size = clip_get_patch_size(ctx_clip);
int32_t num_patches_per_side = image_size / patch_size; // 336 / 14 = 24 - used for embedding-patching boxes (24*24 = 576 patches)
int num_patches_width = grid_shape.first; // grid 1-4
int num_patches_height = grid_shape.second; // grid 1-4
const size_t num_images = num_patches_width * num_patches_height + 1;
// TODO: size calculation is not calculated - it's only tens of MB
size_t ctx_size = 0;
{
ctx_size += clip_embd_nbytes(ctx_clip) * num_images * 8; // image_features
ctx_size += 1024*1024 * ggml_type_size(GGML_TYPE_F32);
}
struct ggml_init_params params {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false, // NOTE: this should be false when using the legacy API
};
// Python reference code for full unpad:
/*
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
image_feature = torch.cat((
image_feature,
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1)
), dim=-1)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
*/
// We now have two options: unpad or no unpad. Unpad removes tokens for faster llm eval.
// In terms of result quality it appears to make no difference, so we'll start with the easier approach given 5D tensors are not supported in ggml yet.
// Without unpad we have to split the sub-image embeddings into patches of 24 features each and permute them.
// Once all images are processed to prepended the base_image_features without any changes.
// Pytorch reference simplified, modified for ggml compatibility - confirmed identical output in python (for a 2x2 grid image (676x676 scaling))
/*
image_feature = image_feature.view(2, 2, 24, 24, 4096)
image_feature = image_feature.permute(0, 2, 1, 3, 4).contiguous()
image_feature = image_feature.view(2, 24, 2, 24, 4096)
image_feature = image_feature.flatten(0, 3)
// Reshape to 4D tensor by merging the last two dimensions
image_feature = image_feature.view(2, 2, 24, 24*4096)
image_feature = image_feature.permute(0, 2, 1, 3).contiguous()
image_feature = image_feature.view(-1, 4096)
*/
model.ctx = ggml_init(params);
struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_output_tokens(ctx_clip, img_input), num_images - 1); // example: 4096 x 576 x 4
// ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
// fill it with the image embeddings, ignoring the base
for (size_t i = 1; i < num_images; i++) {
size_t offset = (i-1) * clip_embd_nbytes(ctx_clip);
memcpy((uint8_t *)(image_features->data) + offset, image_embd_v[i], clip_embd_nbytes(ctx_clip));
}
struct ggml_cgraph * gf = ggml_new_graph(model.ctx);
size_t size_ele = ggml_type_size(GGML_TYPE_F32);
struct ggml_tensor *image_features_patchview = ggml_view_4d(model.ctx, image_features,
num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
num_patches_per_side,
num_patches_width,
num_patches_height,
size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip),
size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side,
size_ele * num_patches_per_side * clip_n_mmproj_embd(ctx_clip) * num_patches_per_side * num_patches_width, 0);
// ggml_tensor_printf(image_features_patchview,"image_features_patchview",__LINE__,false,false);
struct ggml_tensor *permuted_cont = ggml_cont(model.ctx, ggml_permute(model.ctx, image_features_patchview, 0, 2, 1, 3));
/**
At the end of each row we have to add the row_end embeddings, which are the same as the newline embeddings
image_feature = torch.cat((
image_feature,
self.model.image_newline[:, None, None].expand(*image_feature.shape[:-1], 1).to(image_feature.device)
), dim=-1)
*
*/
// ggml_tensor_printf(permuted_cont,"permuted_cont",__LINE__,false,false);
struct ggml_tensor *flatten = ggml_view_2d(model.ctx, permuted_cont, clip_n_mmproj_embd(ctx_clip), num_patches_height * num_patches_width * num_patches_per_side * num_patches_per_side, size_ele * clip_n_mmproj_embd(ctx_clip), 0);
// ggml_tensor_printf(flatten,"flatten",__LINE__,false,false);
ggml_build_forward_expand(gf, flatten);
ggml_graph_compute_with_ctx(model.ctx, gf, 1);
struct ggml_tensor* result = ggml_graph_node(gf, -1);
memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
// append without newline tokens (default behavior in llava_arch when not using unpad ):
memcpy(image_embd_out + clip_n_output_tokens(ctx_clip, img_input) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
*n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_output_tokens(ctx_clip, img_input));
// Debug: Test single segments
// Current findings: sending base image, sending a segment embedding all works similar to python
// However, permuted embeddings do not work yet (stride issue?)
// memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as context
// memcpy(image_embd_out, (float*)prepared_cont->data, clip_embd_nbytes(ctx_clip)); // main image as context
// *n_img_pos_out=576;
ggml_free(model.ctx);
return true;
}
static clip_image_f32 * reshape_by_patch(clip_image_f32 * image, int patch_size) {
int width = image->nx;
int height = image->ny;
int num_patches = (height / patch_size) * (width / patch_size);
clip_image_f32 * patch = clip_image_f32_init();
patch->nx = patch_size * num_patches;
patch->ny = patch_size;
patch->buf.resize(3 * patch->nx * patch->ny);
int patch_index = 0;
for (int i = 0; i < height; i += patch_size) {
for (int j = 0; j < width; j += patch_size) {
for (int pi = 0; pi < patch_size; ++pi) {
for (int pj = 0; pj < patch_size; ++pj) {
int input_index = ((i + pi) * width + (j + pj)) * 3;
int output_index = (pi * patch_size * num_patches + patch_index * patch_size + pj) * 3;
patch->buf[output_index] = image->buf[input_index];
patch->buf[output_index+1] = image->buf[input_index+1];
patch->buf[output_index+2] = image->buf[input_index+2];
}
}
patch_index++;
}
}
return patch;
}
static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float * image_embd, int * n_img_pos) {
// std::vector<clip_image_f32*> img_res_v; // format VectN x H x W x RGB (N x 336 x 336 x 3), so interleaved RGB - different to the python implementation which is N x 3 x 336 x 336
clip_image_f32_batch_ptr img_res_v(clip_image_f32_batch_init());
if (!clip_image_preprocess(ctx_clip, img, img_res_v.get())) {
LOG_ERR("%s: unable to preprocess image\n", __func__);
return false;
}
const int64_t t_img_enc_start_us = ggml_time_us();
const char * mm_patch_merge_type = clip_patch_merge_type(ctx_clip);
const size_t n_imgs = clip_image_f32_batch_n_images(img_res_v.get());
if (clip_is_minicpmv(ctx_clip) || clip_is_qwen2vl(ctx_clip)) {
std::vector<float *> image_embd_v;
image_embd_v.resize(n_imgs);
clip_image_size load_image_size;
for (size_t i = 0; i < n_imgs; i++) {
const int64_t t_img_enc_step_start_us = ggml_time_us();
int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
image_embd_v[i] = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, nx, ny));
int patch_size = 14;
load_image_size.width = nx;
load_image_size.height = ny;
clip_add_load_image_size(ctx_clip, &load_image_size);
bool encoded = false;
clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
if (clip_is_qwen2vl(ctx_clip)) {
encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]);
}
else {
encoded = clip_image_encode(ctx_clip, n_threads, reshape_by_patch(img_res, patch_size), image_embd_v[i]);
}
if (!encoded) {
LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
return false;
}
const int64_t t_img_enc_steop_batch_us = ggml_time_us();
LOG_INF("%s: step %d of %d encoded in %8.2f ms\n", __func__, (int)i+1, (int)n_imgs, (t_img_enc_steop_batch_us - t_img_enc_step_start_us) / 1000.0);
}
const int64_t t_img_enc_batch_us = ggml_time_us();
LOG_INF("%s: all %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
int n_img_pos_out = 0;
for (size_t i = 0; i < image_embd_v.size(); i++) {
int nx = clip_image_f32_batch_nx(img_res_v.get(), i);
int ny = clip_image_f32_batch_ny(img_res_v.get(), i);
clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
std::memcpy(
image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
image_embd_v[i],
clip_embd_nbytes_by_img(ctx_clip, nx, ny));
n_img_pos_out += clip_n_output_tokens(ctx_clip, img_res);
}
*n_img_pos = n_img_pos_out;
for (size_t i = 0; i < image_embd_v.size(); i++) {
free(image_embd_v[i]);
}
image_embd_v.clear();
load_image_size.width = img->nx;
load_image_size.height = img->ny;
clip_add_load_image_size(ctx_clip, &load_image_size);
LOG_INF("%s: load_image_size %d %d\n", __func__, load_image_size.width, load_image_size.height);
}
else if (clip_is_glm(ctx_clip)){
struct clip_image_size * load_image_size = clip_image_size_init();
load_image_size->width = clip_image_f32_batch_nx(img_res_v.get(), 0);
load_image_size->height = clip_image_f32_batch_ny(img_res_v.get(), 0);
clip_add_load_image_size(ctx_clip, load_image_size);
clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd);
int pos = int(load_image_size->width/clip_get_patch_size(ctx_clip)/2);
*n_img_pos = (pos * pos + 2);
if (!encoded){
LOG_ERR("Unable to encode image \n");
return false;
}
}
else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
// flat / default llava-1.5 type embedding
clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
*n_img_pos = clip_n_output_tokens(ctx_clip, img_res);
bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096
if (!encoded) {
LOG_ERR("Unable to encode image\n");
return false;
}
}
else {
// spatial_unpad llava-1.6 type embedding
// TODO: CLIP needs batching support - in HF the llm projection is separate after encoding, which might be a solution to quickly get batching working
std::vector<float *> image_embd_v;
image_embd_v.resize(n_imgs);
for (size_t i = 0; i < n_imgs; i++) {
clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), i);
image_embd_v[i] = (float *)malloc(clip_embd_nbytes(ctx_clip)); // 576 patches * 4096 embeddings * 4 bytes = 9437184
const bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd_v[i]); // image data is in 3x336x336 format and will be converted to 336x336x3 inside
if (!encoded) {
LOG_ERR("Unable to encode image - spatial_unpad - subimage %d of %d\n", (int) i+1, (int) n_imgs);
return false;
}
}
const int64_t t_img_enc_batch_us = ggml_time_us();
LOG_INF("%s: %d segments encoded in %8.2f ms\n", __func__, (int)n_imgs, (t_img_enc_batch_us - t_img_enc_start_us) / 1000.0);
const int32_t * image_grid = clip_image_grid(ctx_clip);
const size_t num_gridpoints = get_clip_image_grid_size(ctx_clip);
std::vector<std::pair<int, int>> grid_pinpoints;
for (size_t i = 0; i < num_gridpoints; i += 2) {
grid_pinpoints.push_back({image_grid[i], image_grid[i+1]});
}
const int32_t image_size = clip_get_image_size(ctx_clip);
struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
int n_img_pos_out;
clip_image_f32 * img_input = clip_image_f32_get_img(img_res_v.get(), 0);
clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out, img_input);
*n_img_pos = n_img_pos_out;
for (size_t i = 0; i < image_embd_v.size(); i++) {
free(image_embd_v[i]);
}
image_embd_v.clear();
// debug image/segment/normalization content:
// clip_image_u8 * tmp = clip_image_u8_init();
// clip_image_convert_f32_to_u8(*image_feature, *tmp);
// clip_image_save_to_bmp(*tmp, "image_feature.bmp");
}
LOG_INF("%s: image embedding created: %d tokens\n", __func__, *n_img_pos);
const int64_t t_img_enc_end_us = ggml_time_us();
float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
LOG_INF("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / *n_img_pos);
return true;
}
bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip) {
// make sure that the correct mmproj was used, i.e., compare apples to apples
int n_llama_embd = llama_model_n_embd(llama_get_model(ctx_llama));
auto n_image_embd = clip_n_mmproj_embd(ctx_clip);
if (n_image_embd != n_llama_embd) {
LOG_ERR("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_image_embd, n_llama_embd);
return false;
}
return true;
}
bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out) {
// Granite vision uses up to 10 patches + base patch
int num_max_patches = 11;
if (clip_is_minicpmv(ctx_clip)) {
num_max_patches = 10;
}
if (clip_is_glm(ctx_clip)) {
num_max_patches = 1;
}
float * image_embd;
if (clip_is_qwen2vl(ctx_clip)) {
// qwen2vl don't split image into chunks, so `num_max_patches` is not needed.
image_embd = (float *)malloc(clip_embd_nbytes_by_img(ctx_clip, img->nx, img->ny));
} else {
image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip)*num_max_patches); // TODO: base on gridsize/llava model
}
if (!image_embd) {
LOG_ERR("Unable to allocate memory for image embeddings\n");
return false;
}
int n_img_pos;
if (!encode_image_with_clip(ctx_clip, n_threads, img, image_embd, &n_img_pos)) {
LOG_ERR("%s: cannot encode image, aborting\n", __func__);
free(image_embd);
return false;
}
*image_embd_out = image_embd;
*n_img_pos_out = n_img_pos;
return true;
}
struct llava_embd_batch {
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id> seq_id_0;
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
logits .resize(n_tokens);
seq_id_0.resize(1);
seq_id_0[0] = seq_id;
seq_ids [n_tokens] = nullptr;
batch = {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
/*logits =*/ logits.data(),
};
for (int i = 0; i < n_tokens; i++) {
batch.pos [i] = pos_0 + i;
batch.n_seq_id[i] = 1;
batch.seq_id [i] = seq_id_0.data();
batch.logits [i] = false;
}
}
};
bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
int n_embd = llama_model_n_embd(llama_get_model(ctx_llama));
for (int i = 0; i < image_embed->n_image_pos; i += n_batch) {
int n_eval = image_embed->n_image_pos - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
float * embd = image_embed->embed+i*n_embd;
llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
if (llama_decode(ctx_llama, llava_batch.batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
*n_past += n_eval;
}
return true;
}
struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length) {
clip_image_u8 * img = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_bytes, image_bytes_length, img)) {
clip_image_u8_free(img);
LOG_ERR("%s: can't load image from bytes, is it a valid image?", __func__);
return NULL;
}
float* image_embed = NULL;
int n_image_pos = 0;
bool image_embed_result = llava_image_embed_make_with_clip_img(ctx_clip, n_threads, img, &image_embed, &n_image_pos);
if (!image_embed_result) {
clip_image_u8_free(img);
LOG_ERR("%s: couldn't embed the image\n", __func__);
return NULL;
}
clip_image_u8_free(img);
auto result = (llava_image_embed*)malloc(sizeof(llava_image_embed));
result->embed = image_embed;
result->n_image_pos = n_image_pos;
return result;
}
static bool load_file_to_bytes(const char* path, unsigned char** bytesOut, long *sizeOut) {
auto file = fopen(path, "rb");
if (file == NULL) {
LOG_ERR("%s: can't read file %s\n", __func__, path);
return false;
}
fseek(file, 0, SEEK_END);
auto fileSize = ftell(file);
fseek(file, 0, SEEK_SET);
auto buffer = (unsigned char *)malloc(fileSize); // Allocate memory to hold the file data
if (buffer == NULL) {
LOG_ERR("%s: failed to alloc %ld bytes for file %s\n", __func__, fileSize, path);
perror("Memory allocation error");
fclose(file);
return false;
}
errno = 0;
size_t ret = fread(buffer, 1, fileSize, file); // Read the file into the buffer
if (ferror(file)) {
LOG_ERR("read error: %s", strerror(errno));
free(buffer);
fclose(file);
return false;
}
if (ret != (size_t) fileSize) {
LOG_ERR("unexpectedly reached end of file");
free(buffer);
fclose(file);
return false;
}
fclose(file); // Close the file
*bytesOut = buffer;
*sizeOut = fileSize;
return true;
}
struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path) {
unsigned char* image_bytes;
long image_bytes_length;
auto loaded = load_file_to_bytes(image_path, &image_bytes, &image_bytes_length);
if (!loaded) {
LOG_ERR("%s: failed to load %s\n", __func__, image_path);
return NULL;
}
llava_image_embed *embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, image_bytes, image_bytes_length);
free(image_bytes);
return embed;
}
void llava_image_embed_free(struct llava_image_embed * embed) {
free(embed->embed);
free(embed);
}

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#ifndef LLAVA_H
#define LLAVA_H
#include "ggml.h"
#ifdef LLAMA_SHARED
# if defined(_WIN32) && !defined(__MINGW32__)
# ifdef LLAMA_BUILD
# define LLAVA_API __declspec(dllexport)
# else
# define LLAVA_API __declspec(dllimport)
# endif
# else
# define LLAVA_API __attribute__ ((visibility ("default")))
# endif
#else
# define LLAVA_API
#endif
#ifdef __cplusplus
extern "C" {
#endif
struct clip_ctx;
struct llava_image_embed {
float * embed;
int n_image_pos;
};
/** sanity check for clip <-> llava embed size match */
LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
/** build an image embed from image file bytes */
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
/** build an image embed from a path to an image filename */
LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
/** free an embedding made with llava_image_embed_make_* */
LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
#ifdef __cplusplus
}
#endif
#endif

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import argparse
import glob
import os
import torch
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to LLaVA v1.5 model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
path = sorted(glob.glob(f"{args.model}/pytorch_model*.bin"))[-1]
checkpoint = torch.load(path)
# get a list of mm tensor names
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_projector")]
# store these tensors in a new dictionary and torch.save them
projector = {name: checkpoint[name].float() for name in mm_tensors}
torch.save(projector, f"{args.model}/llava.projector")
# BakLLaVA models contain CLIP tensors in it
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")]
if len(clip_tensors) > 0:
clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors}
torch.save(clip, f"{args.model}/llava.clip")
# added tokens should be removed to be able to convert Mistral models
if os.path.exists(f"{args.model}/added_tokens.json"):
with open(f"{args.model}/added_tokens.json", "w") as f:
f.write("{}\n")
print("Done!")
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")

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import argparse
import glob
import os
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from typing import Any, ContextManager, cast
# Function to determine if file is a SafeTensor file
def is_safetensor_file(file_path):
return file_path.endswith('.safetensors')
# Unified loading function
def load_model(file_path):
if is_safetensor_file(file_path):
tensors = {}
with cast(ContextManager[Any], safe_open(file_path, framework="pt", device="cpu")) as f:
for key in f.keys():
tensors[key] = f.get_tensor(key).clone()
# output shape
print(f"{key} : {tensors[key].shape}")
return tensors, 'safetensor'
else:
return torch.load(file_path, map_location=torch.device('cpu')), 'pytorch'
# Unified saving function
def save_model(model, file_path, file_type):
if file_type == 'safetensor':
# safe_save(model, file_path)
save_file(model, file_path)
else:
torch.save(model, file_path)
# Helpers to match weight names from specific components or
# determine if a saved shard contains that component
def is_vision_tower(weight_name):
return (
weight_name.startswith("model.vision_tower") or
weight_name.startswith("vit.") or
weight_name.startswith("vision_tower")
)
def is_newline(weight_name):
return (
weight_name.startswith("model.image_newline") or
weight_name.startswith("image_newline")
)
def is_mm_projector(weight_name):
return (
weight_name.startswith("model.mm_projector") or
weight_name.startswith("vision_proj.") or
weight_name.startswith("multi_modal_projector")
)
def newline_criteria(checkpoint):
return any(is_newline(k) for k in checkpoint.keys())
def proj_criteria(checkpoint):
return any(is_mm_projector(k) for k in checkpoint.keys())
# Adapted function to clean vision tower from checkpoint
def clean_vision_tower_from_checkpoint(checkpoint_path):
checkpoint, file_type = load_model(checkpoint_path)
# file_type = 'pytorch'
model_path = os.path.dirname(checkpoint_path)
print(f"Searching for vision tower tensors in {checkpoint_path}")
clip_tensors = [k for k, v in checkpoint.items() if is_vision_tower(k)]
if len(clip_tensors) > 0:
print(f"Found {len(clip_tensors)} tensors to extract from {checkpoint_path}")
# Adapted for file type
clip_path = os.path.join(model_path, "llava.clip")
if os.path.exists(clip_path):
print(f"Loading existing llava.clip from {clip_path}")
existing_clip, _ = load_model(clip_path)
else:
print(f"Creating new llava.clip at {clip_path}")
existing_clip = {}
# Update existing_clip with new tensors, avoid duplicates
for name in clip_tensors:
simple_name = name[name.index('vision_model.'):] if 'vision_model.' in name else name
print(f"Adding {simple_name} to llava.clip")
if simple_name not in existing_clip:
existing_clip[simple_name] = checkpoint[name]
# Save the updated clip tensors back to llava.clip
save_model(existing_clip, clip_path, 'pytorch')
# Remove the tensors from the original checkpoint
for name in clip_tensors:
del checkpoint[name]
checkpoint_path = checkpoint_path
return True
return False
def find_relevant_checkpoints(checkpoint_paths, newline_criteria, projector):
newline_checkpoint_path = None
projector_checkpoint_path = None
for path in checkpoint_paths:
checkpoint, _ = load_model(path)
if newline_criteria(checkpoint) and newline_checkpoint_path is None:
newline_checkpoint_path = path
if projector(checkpoint):
projector_checkpoint_path = path
return newline_checkpoint_path, projector_checkpoint_path
# Command-line interface setup
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True, help="Path to LLaVA v1.5+ model")
ap.add_argument("-C", "--clean-vision-tower", action="store_true", help="Remove any vision tower from the model files")
args = ap.parse_args()
if args.clean_vision_tower:
# Generalized to handle both PyTorch and SafeTensors models
model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
# checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and path.startswith('pytorch')) or (path.endswith('.safetensors') and path.startswith('model'))]
checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
for projector_checkpoint_path in checkpoint_paths:
print(f"Cleaning {projector_checkpoint_path}")
if not clean_vision_tower_from_checkpoint(projector_checkpoint_path):
print(f"No vision tower found in {projector_checkpoint_path}")
# we break once none is found, so far all models append them at the end
# break
print("Done! All vision tower tensors are removed from the model files and stored in llava.clip file.")
# Now we look for the projector in the last checkpoint
model_files = sorted(glob.glob(f"{args.model}/*"), key=os.path.getmtime, reverse=True)
checkpoint_paths = [path for path in model_files if (path.endswith('.bin') and 'pytorch' in path.split('/')[-1].split('\\')[-1]) or (path.endswith('.safetensors') and 'model' in path.split('/')[-1].split('\\')[-1])]
# last_checkpoint_path = checkpoint_paths[0]
# first_checkpoint_path = checkpoint_paths[-1]
newline_checkpoint_path, projector_checkpoint_path = find_relevant_checkpoints(checkpoint_paths, newline_criteria, proj_criteria)
print(f"Taking projector from {projector_checkpoint_path}")
first_mm_tensors = []
first_checkpoint = None
if newline_checkpoint_path is not None:
print(f"Taking newline from {newline_checkpoint_path}")
first_checkpoint, file_type = load_model(newline_checkpoint_path)
first_mm_tensors = [k for k, v in first_checkpoint.items() if is_newline(k)]
# Load the checkpoint
mm_tensors = []
last_checkpoint = None
if projector_checkpoint_path is not None:
last_checkpoint, file_type = load_model(projector_checkpoint_path)
mm_tensors = [k for k, v in last_checkpoint.items() if is_mm_projector(k)]
if len(mm_tensors) == 0:
if last_checkpoint is not None:
for k, v in last_checkpoint.items():
print(k)
print(f"Found {len(mm_tensors)} tensors to extract out of {len(last_checkpoint) if last_checkpoint is not None else 0} tensors.")
print("No tensors found. Is this a LLaVA model?")
exit()
print(f"Found {len(mm_tensors)} tensors to extract.")
print(f"Found additional {len(first_mm_tensors)} tensors to extract.")
# projector = {name: checkpoint.[name].float() for name in mm_tensors}
projector = {}
for name in mm_tensors:
assert last_checkpoint is not None
projector[name] = last_checkpoint[name].float()
for name in first_mm_tensors:
assert first_checkpoint is not None
projector[name] = first_checkpoint[name].float()
if len(projector) > 0:
save_model(projector, f"{args.model}/llava.projector", 'pytorch')
print("Done!")
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")

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# coding=utf-8
# Copyright 2024 Google AI and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" PyTorch Siglip model. """
# Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
import os
import math
import warnings
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn.init import _calculate_fan_in_and_fan_out
from transformers.activations import ACT2FN
from transformers.modeling_utils import PreTrainedModel
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import (
logging,
)
from transformers.utils import logging
logger = logging.get_logger(__name__)
class SiglipVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip
[google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input images.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 16):
The size (resolution) of each patch.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` ``"quick_gelu"` are supported.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
Example:
```python
>>> from transformers import SiglipVisionConfig, SiglipVisionModel
>>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
>>> configuration = SiglipVisionConfig()
>>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
>>> model = SiglipVisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "siglip_vision_model"
def __init__(
self,
hidden_size=768,
intermediate_size=3072,
num_hidden_layers=12,
num_attention_heads=12,
num_channels=3,
image_size=224,
patch_size=16,
hidden_act="gelu_pytorch_tanh",
layer_norm_eps=1e-6,
attention_dropout=0.0,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/siglip-base-patch16-224",
# See all SigLIP models at https://huggingface.co/models?filter=siglip
]
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
max_seqlen_in_batch = seqlens_in_batch.max().item()
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
return (
indices,
cu_seqlens,
max_seqlen_in_batch,
)
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
def norm_cdf(x):
# Computes standard normal cumulative distribution function
return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
if (mean < a - 2 * std) or (mean > b + 2 * std):
warnings.warn(
"mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
"The distribution of values may be incorrect.",
stacklevel=2,
)
# Values are generated by using a truncated uniform distribution and
# then using the inverse CDF for the normal distribution.
# Get upper and lower cdf values
l = norm_cdf((a - mean) / std)
u = norm_cdf((b - mean) / std)
# Uniformly fill tensor with values from [l, u], then translate to
# [2l-1, 2u-1].
tensor.uniform_(2 * l - 1, 2 * u - 1)
# Use inverse cdf transform for normal distribution to get truncated
# standard normal
if tensor.dtype in [torch.float16, torch.bfloat16]:
# The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
og_dtype = tensor.dtype
tensor = tensor.to(torch.float32)
tensor.erfinv_()
tensor = tensor.to(og_dtype)
else:
tensor.erfinv_()
# Transform to proper mean, std
tensor.mul_(std * math.sqrt(2.0))
tensor.add_(mean)
# Clamp to ensure it's in the proper range
if tensor.dtype == torch.float16:
# The `clamp_` op is not (yet?) defined in float16+cpu
tensor = tensor.to(torch.float32)
tensor.clamp_(min=a, max=b)
tensor = tensor.to(torch.float16)
else:
tensor.clamp_(min=a, max=b)
def trunc_normal_tf_(
tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
):
"""Fills the input Tensor with values drawn from a truncated
normal distribution. The values are effectively drawn from the
normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
with values outside :math:`[a, b]` redrawn until they are within
the bounds. The method used for generating the random values works
best when :math:`a \\leq \text{mean} \\leq b`.
NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
and the result is subsquently scaled and shifted by the mean and std args.
Args:
tensor: an n-dimensional `torch.Tensor`
mean: the mean of the normal distribution
std: the standard deviation of the normal distribution
a: the minimum cutoff value
b: the maximum cutoff value
"""
with torch.no_grad():
_trunc_normal_(tensor, 0, 1.0, a, b)
tensor.mul_(std).add_(mean)
def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
denom = fan_in
if mode == "fan_in":
denom = fan_in
elif mode == "fan_out":
denom = fan_out
elif mode == "fan_avg":
denom = (fan_in + fan_out) / 2
variance = scale / denom
if distribution == "truncated_normal":
# constant is stddev of standard normal truncated to (-2, 2)
trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
elif distribution == "normal":
with torch.no_grad():
tensor.normal_(std=math.sqrt(variance))
elif distribution == "uniform":
bound = math.sqrt(3 * variance)
with torch.no_grad():
tensor.uniform_(-bound, bound)
else:
raise ValueError(f"invalid distribution {distribution}")
def lecun_normal_(tensor):
variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
def default_flax_embed_init(tensor):
variance_scaling_(tensor, mode="fan_in", distribution="normal")
class SiglipVisionEmbeddings(nn.Module):
def __init__(self, config: SiglipVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.patch_embedding = nn.Conv2d(
in_channels=config.num_channels,
out_channels=self.embed_dim,
kernel_size=self.patch_size,
stride=self.patch_size,
padding="valid",
)
self.num_patches_per_side = self.image_size // self.patch_size
self.num_patches = self.num_patches_per_side**2
self.num_positions = self.num_patches
self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
class SiglipAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
# Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
f" {self.num_heads})."
)
self.scale = self.head_dim**-0.5
self.dropout = config.attention_dropout
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
class SiglipMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip
class SiglipEncoderLayer(nn.Module):
def __init__(self, config: SiglipVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
self.self_attn = (
SiglipAttention(config)
)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = SiglipMLP(config)
self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
class SiglipPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SiglipVisionConfig
base_model_prefix = "siglip"
supports_gradient_checkpointing = True
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, SiglipVisionEmbeddings):
width = self.config.hidden_size
nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width))
elif isinstance(module, nn.Embedding):
default_flax_embed_init(module.weight)
elif isinstance(module, SiglipAttention):
nn.init.normal_(module.q_proj.weight)
nn.init.normal_(module.k_proj.weight)
nn.init.normal_(module.v_proj.weight)
nn.init.normal_(module.out_proj.weight)
nn.init.zeros_(module.q_proj.bias)
nn.init.zeros_(module.k_proj.bias)
nn.init.zeros_(module.v_proj.bias)
nn.init.zeros_(module.out_proj.bias)
elif isinstance(module, SiglipMLP):
nn.init.normal_(module.fc1.weight)
nn.init.normal_(module.fc2.weight)
nn.init.normal_(module.fc1.bias, std=1e-6)
nn.init.normal_(module.fc2.bias, std=1e-6)
elif isinstance(module, (nn.Linear, nn.Conv2d)):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
SIGLIP_START_DOCSTRING = r"""
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`SiglipVisionConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
SIGLIP_VISION_INPUTS_DOCSTRING = r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip
class SiglipEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`SiglipEncoderLayer`].
Args:
config: SiglipConfig
"""
def __init__(self, config: SiglipVisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
class SiglipVisionTransformer(SiglipPreTrainedModel):
config_class = SiglipVisionConfig
main_input_name = "pixel_values"
_supports_flash_attn_2 = True
def __init__(self, config: SiglipVisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = SiglipVisionEmbeddings(config)
self.encoder = SiglipEncoder(config)
self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.embeddings.patch_embedding
import argparse
import json
import re
import numpy as np
from gguf import *
from transformers.models.idefics2.modeling_idefics2 import Idefics2VisionTransformer, Idefics2VisionConfig
TEXT = "clip.text"
VISION = "clip.vision"
def add_key_str(raw_key: str, arch: str) -> str:
return raw_key.format(arch=arch)
def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_minicpmv: bool) -> bool:
if name in (
"logit_scale",
"text_model.embeddings.position_ids",
"vision_model.embeddings.position_ids",
):
return True
if has_minicpmv and name in ["visual_projection.weight"]:
return True
if name.startswith("v") and not has_vision:
return True
if name.startswith("t") and not has_text:
return True
return False
def get_tensor_name(name: str) -> str:
if "projection" in name:
return name
if "mm_projector" in name:
name = name.replace("model.mm_projector", "mm")
name = re.sub(r'mm\.mlp\.mlp', 'mm.model.mlp', name, count=1)
name = re.sub(r'mm\.peg\.peg', 'mm.model.peg', name, count=1)
return name
return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = (
list(range(ord("!"), ord("~") + 1))
+ list(range(ord("¡"), ord("¬") + 1))
+ list(range(ord("®"), ord("ÿ") + 1))
)
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8 + n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip-model-is-vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--clip-model-is-openclip", action="store_true", required=False,
help="The clip model is from openclip (for ViT-SO400M type))")
ap.add_argument("--minicpmv-projector", help="Path to minicpmv.projector file. If specified, save an image encoder for MiniCPM-V models.")
ap.add_argument("--projector-type", help="Type of projector. Possible values: mlp, ldp, ldpv2", choices=["mlp", "ldp", "ldpv2"], default="mlp")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
# Example --image_mean 0.5 0.5 0.5 --image_std 0.5 0.5 0.5
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image-mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image-std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
ap.add_argument('--minicpmv_version', type=int, help='minicpmv_version: MiniCPM-V-2 use 1; MiniCPM-V-2.5 use 2; MiniCPM-V-2.6 use 3; MiniCPM-o-2.6 use 4', default=2)
# with proper
args = ap.parse_args()
if args.text_only and args.vision_only:
print("--text-only and --image-only arguments cannot be specified at the same time.")
exit(1)
if args.use_f32:
print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if args.clip_model_is_vision or not os.path.exists(dir_model + "/vocab.json") or args.clip_model_is_openclip:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
# possible data types
# ftype == 0 -> float32
# ftype == 1 -> float16
#
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if args.use_f32:
ftype = 0
# if args.clip_model_is_vision or args.clip_model_is_openclip:
# model = CLIPVisionModel.from_pretrained(dir_model)
# processor = None
# else:
# model = CLIPModel.from_pretrained(dir_model)
# processor = CLIPProcessor.from_pretrained(dir_model)
minicpmv_version = args.minicpmv_version
emb_dim = 4096
block_count = 26
if minicpmv_version == 1:
emb_dim = 2304
block_count = 26
elif minicpmv_version == 2:
emb_dim = 4096
block_count = 27
elif minicpmv_version == 3:
emb_dim = 3584
block_count = 27
elif minicpmv_version == 4:
emb_dim = 3584
block_count = 27
default_vision_config = {
"hidden_size": 1152,
"image_size": 980,
"intermediate_size": 4304,
"model_type": "idefics2",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"patch_size": 14,
}
vision_config = Idefics2VisionConfig(**default_vision_config)
model = Idefics2VisionTransformer(vision_config)
if minicpmv_version == 3:
vision_config = SiglipVisionConfig(**default_vision_config)
model = SiglipVisionTransformer(vision_config)
elif minicpmv_version == 4:
vision_config = SiglipVisionConfig(**default_vision_config)
model = SiglipVisionTransformer(vision_config)
processor = None
# if model.attn_pool is not None:
# model.attn_pool = torch.nn.Identity()
# model.blocks = model.blocks[:-1]
model.load_state_dict(torch.load(os.path.join(dir_model, "minicpmv.clip")))
fname_middle = None
has_text_encoder = True
has_vision_encoder = True
has_minicpmv_projector = False
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.minicpmv_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_minicpmv_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
output_dir = args.output_dir if args.output_dir is not None else dir_model
os.makedirs(output_dir, exist_ok=True)
output_prefix = os.path.basename(output_dir).replace("ggml_", "")
fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
fout = GGUFWriter(path=fname_out, arch="clip")
fout.add_bool("clip.has_text_encoder", has_text_encoder)
fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
fout.add_bool("clip.has_minicpmv_projector", has_minicpmv_projector)
fout.add_file_type(ftype)
if args.text_only:
fout.add_description("text-only CLIP model")
elif args.vision_only and not has_minicpmv_projector:
fout.add_description("vision-only CLIP model")
elif has_minicpmv_projector:
fout.add_description("image encoder for MiniCPM-V")
# add projector type
fout.add_string("clip.projector_type", "resampler")
fout.add_int32("clip.minicpmv_version", minicpmv_version)
else:
fout.add_description("two-tower CLIP model")
if has_vision_encoder:
# vision_model hparams
fout.add_uint32("clip.vision.image_size", 448)
fout.add_uint32("clip.vision.patch_size", 14)
fout.add_uint32(add_key_str(KEY_EMBEDDING_LENGTH, VISION), 1152)
fout.add_uint32(add_key_str(KEY_FEED_FORWARD_LENGTH, VISION), 4304)
fout.add_uint32("clip.vision.projection_dim", 0)
fout.add_uint32(add_key_str(KEY_ATTENTION_HEAD_COUNT, VISION), 16)
fout.add_float32(add_key_str(KEY_ATTENTION_LAYERNORM_EPS, VISION), 1e-6)
fout.add_uint32(add_key_str(KEY_BLOCK_COUNT, VISION), block_count)
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
else:
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)
use_gelu = True
fout.add_bool("clip.use_gelu", use_gelu)
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float32)
omega /= embed_dim / 2.
omega = 1. / 10000 ** omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
# https://github.com/facebookresearch/mae/blob/efb2a8062c206524e35e47d04501ed4f544c0ae8/util/pos_embed.py#L20
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
if isinstance(grid_size, int):
grid_h_size, grid_w_size = grid_size, grid_size
else:
grid_h_size, grid_w_size = grid_size[0], grid_size[1]
grid_h = np.arange(grid_h_size, dtype=np.float32)
grid_w = np.arange(grid_w_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_h_size, grid_w_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token:
pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
return pos_embed
def _replace_name_resampler(s, v):
if re.match("resampler.pos_embed", s):
return {
s: v,
re.sub("pos_embed", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(emb_dim, (70, 70))),
}
if re.match("resampler.proj", s):
return {
re.sub("proj", "pos_embed_k", s): torch.from_numpy(get_2d_sincos_pos_embed(emb_dim, (70, 70))),
re.sub("proj", "proj.weight", s): v.transpose(-1, -2).contiguous(),
}
if re.match("resampler.attn.in_proj_.*", s):
return {
re.sub("attn.in_proj_", "attn.q.", s): v.chunk(3, dim=0)[0],
re.sub("attn.in_proj_", "attn.k.", s): v.chunk(3, dim=0)[1],
re.sub("attn.in_proj_", "attn.v.", s): v.chunk(3, dim=0)[2],
}
return {s: v}
if has_minicpmv_projector:
projector = torch.load(args.minicpmv_projector)
new_state_dict = {}
for k, v in projector.items():
kvs = _replace_name_resampler(k, v)
for nk, nv in kvs.items():
new_state_dict[nk] = nv
projector = new_state_dict
ftype_cur = 0
for name, data in projector.items():
name = get_tensor_name(name)
data = data.squeeze().numpy()
n_dims = len(data.shape)
if ftype == 1:
if name[-7:] == ".weight" and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
fout.add_tensor(name, data)
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
print("Projector tensors added\n")
def _replace_name(s, v):
s = "vision_model." + s
if re.match("vision_model.embeddings.position_embedding", s):
v = v.unsqueeze(0)
return {s: v}
return {s: v}
state_dict = model.state_dict()
new_state_dict = {}
for k, v in state_dict.items():
kvs = _replace_name(k, v)
for nk, nv in kvs.items():
new_state_dict[nk] = nv
state_dict = new_state_dict
for name, data in state_dict.items():
if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_minicpmv_projector):
# we don't need this
print(f"skipping parameter: {name}")
continue
name = get_tensor_name(name)
data = data.squeeze().numpy()
n_dims = len(data.shape)
# ftype == 0 -> float32, ftype == 1 -> float16
ftype_cur = 0
if n_dims == 4:
print(f"tensor {name} is always saved in f16")
data = data.astype(np.float16)
ftype_cur = 1
elif ftype == 1:
if name[-7:] == ".weight" and n_dims == 2:
print(" Converting to float16")
data = data.astype(np.float16)
ftype_cur = 1
else:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
else:
if data.dtype != np.float32:
print(" Converting to float32")
data = data.astype(np.float32)
ftype_cur = 0
print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
fout.add_tensor(name, data)
fout.write_header_to_file()
fout.write_kv_data_to_file()
fout.write_tensors_to_file()
fout.close()
print("Done. Output file: " + fname_out)

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import argparse
import os
import torch
from transformers import AutoModel, AutoTokenizer
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to MiniCPM-V model")
args = ap.parse_args()
# find the model part that includes the the multimodal projector weights
model = AutoModel.from_pretrained(args.model, trust_remote_code=True, local_files_only=True, torch_dtype=torch.bfloat16)
checkpoint = model.state_dict()
# get a list of mm tensor names
mm_tensors = [k for k, v in checkpoint.items() if k.startswith("resampler")]
# store these tensors in a new dictionary and torch.save them
projector = {name: checkpoint[name].float() for name in mm_tensors}
torch.save(projector, f"{args.model}/minicpmv.projector")
clip_tensors = [k for k, v in checkpoint.items() if k.startswith("vpm")]
if len(clip_tensors) > 0:
clip = {name.replace("vpm.", ""): checkpoint[name].float() for name in clip_tensors}
torch.save(clip, f"{args.model}/minicpmv.clip")
# added tokens should be removed to be able to convert Mistral models
if os.path.exists(f"{args.model}/added_tokens.json"):
with open(f"{args.model}/added_tokens.json", "w") as f:
f.write("{}\n")
config = model.llm.config
config.auto_map = {
"AutoConfig": "configuration_minicpm.MiniCPMConfig",
"AutoModel": "modeling_minicpm.MiniCPMModel",
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
}
model.llm.save_pretrained(f"{args.model}/model")
tok = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
tok.save_pretrained(f"{args.model}/model")
print("Done!")
print(f"Now you can convert {args.model} to a regular LLaMA GGUF file.")
print(f"Also, use {args.model}/minicpmv.projector to prepare a minicpmv-encoder.gguf file.")

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#include "arg.h"
#include "log.h"
#include "common.h"
#include "sampling.h"
#include "llama.h"
#include "ggml.h"
#include "console.h"
#include "chat.h"
#include "mtmd.h"
#include <vector>
#include <limits.h>
#include <cinttypes>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <signal.h>
#endif
// volatile, because of signal being an interrupt
static volatile bool g_is_generating = false;
static volatile bool g_is_interrupted = false;
/**
* Please note that this is NOT a production-ready stuff.
* It is a playground for trying multimodal support in llama.cpp.
* For contributors: please keep this code simple and easy to understand.
*/
static void show_additional_info(int /*argc*/, char ** argv) {
LOG(
"Experimental CLI for multimodal\n\n"
"Usage: %s [options] -m <model> --mmproj <mmproj> --image <image> -p <prompt>\n\n"
" -m and --mmproj are required\n"
" -hf user/repo can replace both -m and --mmproj in most cases\n"
" --image and -p are optional, if NOT provided, the CLI will run in chat mode\n"
" to disable using GPU for mmproj model, add --no-mmproj-offload\n",
argv[0]
);
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
static void sigint_handler(int signo) {
if (signo == SIGINT) {
if (g_is_generating) {
g_is_generating = false;
} else {
console::cleanup();
if (g_is_interrupted) {
_exit(1);
}
g_is_interrupted = true;
}
}
}
#endif
struct mtmd_cli_context {
mtmd_context_ptr ctx_vision;
common_init_result llama_init;
llama_model * model;
llama_context * lctx;
const llama_vocab * vocab;
llama_batch batch;
int n_batch;
std::vector<mtmd_bitmap> bitmaps;
// note: we know that gemma3 template is "linear", meaning each turn is completely separated to another
// so here we don't need to keep track of chat history
common_chat_templates_ptr tmpls;
// support for legacy templates (models not having EOT token)
llama_tokens antiprompt_tokens;
int n_threads = 1;
llama_pos n_past = 0;
mtmd_cli_context(common_params & params) : llama_init(common_init_from_params(params)) {
model = llama_init.model.get();
lctx = llama_init.context.get();
vocab = llama_model_get_vocab(model);
n_threads = params.cpuparams.n_threads;
batch = llama_batch_init(params.n_batch, 0, 1);
n_batch = params.n_batch;
if (!llama_model_chat_template(model, nullptr) && params.chat_template.empty()) {
LOG_ERR("Model does not have chat template.\n");
LOG_ERR(" For old llava models, you may need to use '--chat-template vicuna'\n");
LOG_ERR(" For MobileVLM models, use '--chat-template deepseek'\n");
LOG_ERR(" For Mistral Small 3.1, use '--chat-template mistral-v7'\n");
exit(1);
}
tmpls = common_chat_templates_init(model, params.chat_template);
LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(tmpls.get(), params.use_jinja).c_str());
init_vision_context(params);
// load antiprompt tokens for legacy templates
if (params.chat_template == "vicuna") {
antiprompt_tokens = common_tokenize(lctx, "ASSISTANT:", false, true);
} else if (params.chat_template == "deepseek") {
antiprompt_tokens = common_tokenize(lctx, "###", false, true);
}
}
void init_vision_context(common_params & params) {
const char * clip_path = params.mmproj.path.c_str();
ctx_vision.reset(mtmd_init_from_file(clip_path, model, mtmd_context_params{
/* use_gpu */ params.mmproj_use_gpu,
/* timings */ true,
/* n_threads */ params.cpuparams.n_threads,
/* verbosity */ params.verbosity > 0 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_INFO,
}));
if (!ctx_vision.get()) {
LOG_ERR("Failed to load vision model from %s\n", clip_path);
exit(1);
}
}
bool check_antiprompt(const llama_tokens & generated_tokens) {
if (antiprompt_tokens.empty() || generated_tokens.size() < antiprompt_tokens.size()) {
return false;
}
return std::equal(
generated_tokens.end() - antiprompt_tokens.size(),
generated_tokens.end(),
antiprompt_tokens.begin()
);
}
bool load_image(const std::string & fname) {
mtmd_bitmap bitmap;
if (mtmd_helper_bitmap_init_from_file(fname.c_str(), bitmap)) {
return false;
}
bitmaps.push_back(std::move(bitmap));
return true;
}
};
static int generate_response(mtmd_cli_context & ctx, common_sampler * smpl, int n_predict) {
llama_tokens generated_tokens;
for (int i = 0; i < n_predict; i++) {
if (i > n_predict || !g_is_generating || g_is_interrupted) {
LOG("\n");
break;
}
llama_token token_id = common_sampler_sample(smpl, ctx.lctx, -1);
generated_tokens.push_back(token_id);
common_sampler_accept(smpl, token_id, true);
if (llama_vocab_is_eog(ctx.vocab, token_id) || ctx.check_antiprompt(generated_tokens)) {
LOG("\n");
break; // end of generation
}
LOG("%s", common_token_to_piece(ctx.lctx, token_id).c_str());
fflush(stdout);
if (g_is_interrupted) {
LOG("\n");
break;
}
// eval the token
common_batch_clear(ctx.batch);
common_batch_add(ctx.batch, token_id, ctx.n_past++, {0}, true);
if (llama_decode(ctx.lctx, ctx.batch)) {
LOG_ERR("failed to decode token\n");
return 1;
}
}
return 0;
}
static int eval_message(mtmd_cli_context & ctx, common_chat_msg & msg, bool add_bos = false) {
common_chat_templates_inputs tmpl_inputs;
tmpl_inputs.messages = {msg};
tmpl_inputs.add_generation_prompt = true;
tmpl_inputs.use_jinja = false; // jinja is buggy here
auto formatted_chat = common_chat_templates_apply(ctx.tmpls.get(), tmpl_inputs);
LOG_DBG("formatted_chat.prompt: %s\n", formatted_chat.prompt.c_str());
mtmd_input_text text;
text.text = formatted_chat.prompt;
text.add_special = add_bos;
text.parse_special = true;
mtmd_input_chunks chunks;
if (g_is_interrupted) return 0;
int32_t res = mtmd_tokenize(ctx.ctx_vision.get(), chunks, text, ctx.bitmaps);
if (res != 0) {
LOG_ERR("Unable to tokenize prompt, res = %d\n", res);
return 1;
}
ctx.bitmaps.clear();
if (mtmd_helper_eval(ctx.ctx_vision.get(), ctx.lctx, chunks, ctx.n_past, 0, ctx.n_batch)) {
LOG_ERR("Unable to eval prompt\n");
return 1;
}
ctx.n_past += mtmd_helper_get_n_pos(chunks);
LOG("\n");
return 0;
}
int main(int argc, char ** argv) {
ggml_time_init();
common_params params;
params.sampling.temp = 0.2; // lower temp by default for better quality
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, show_additional_info)) {
return 1;
}
common_init();
if (params.mmproj.path.empty()) {
show_additional_info(argc, argv);
LOG_ERR("ERR: Missing --mmproj argument\n");
return 1;
}
mtmd_cli_context ctx(params);
LOG("%s: loading model: %s\n", __func__, params.model.path.c_str());
bool is_single_turn = !params.prompt.empty() && !params.image.empty();
struct common_sampler * smpl = common_sampler_init(ctx.model, params.sampling);
int n_predict = params.n_predict < 0 ? INT_MAX : params.n_predict;
// ctrl+C handling
{
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = sigint_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
}
if (g_is_interrupted) return 130;
if (is_single_turn) {
g_is_generating = true;
if (params.prompt.find("<__image__>") == std::string::npos) {
params.prompt += " <__image__>";
}
common_chat_msg msg;
msg.role = "user";
msg.content = params.prompt;
for (const auto & image : params.image) {
if (!ctx.load_image(image)) {
return 1; // error is already printed by libmtmd
}
}
if (eval_message(ctx, msg, true)) {
return 1;
}
if (!g_is_interrupted && generate_response(ctx, smpl, n_predict)) {
return 1;
}
} else {
LOG("\n Running in chat mode, available commands:");
LOG("\n /image <path> load an image");
LOG("\n /clear clear the chat history");
LOG("\n /quit or /exit exit the program");
LOG("\n");
bool is_first_msg = true;
std::string content;
while (!g_is_interrupted) {
g_is_generating = false;
LOG("\n> ");
console::set_display(console::user_input);
std::string line;
console::readline(line, false);
if (g_is_interrupted) break;
console::set_display(console::reset);
line = string_strip(line);
if (line.empty()) {
continue;
}
if (line == "/quit" || line == "/exit") {
break;
}
if (line == "/clear") {
ctx.n_past = 0;
llama_kv_self_seq_rm(ctx.lctx, 0, 1, -1); // keep BOS
LOG("Chat history cleared\n\n");
continue;
}
g_is_generating = true;
if (line == "/image" || line.find("/image ") == 0) {
if (line.size() < 8) {
LOG_ERR("ERR: Missing image filename\n");
continue;
}
std::string image = line.substr(7);
if (ctx.load_image(image)) {
LOG("Image %s loaded\n", image.c_str());
content += "<__image__>";
}
// else, error is already printed by libmtmd
continue;
} else {
content += line;
}
common_chat_msg msg;
msg.role = "user";
msg.content = content;
int ret = eval_message(ctx, msg, is_first_msg);
if (ret) {
return 1;
}
if (g_is_interrupted) break;
if (generate_response(ctx, smpl, n_predict)) {
return 1;
}
content.clear();
is_first_msg = false;
}
}
if (g_is_interrupted) LOG("\nInterrupted by user\n");
LOG("\n\n");
llama_perf_context_print(ctx.lctx);
return g_is_interrupted ? 130 : 0;
}

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#include "clip.h"
#include "clip-impl.h"
#include "mtmd.h"
#include "llama.h"
#include <algorithm>
#include <cerrno>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <vector>
// slice template, used by some llava-uhd models to correctly place the special tokens around image embeddings
// models not having it (llava-1.6) will process embeddings without any special tokens in-between
enum mtmd_slice_tmpl {
MTMD_SLICE_TMPL_NONE,
MTMD_SLICE_TMPL_MINICPMV_2_5,
MTMD_SLICE_TMPL_MINICPMV_2_6,
// TODO @ngxson : add support for idefics (SmolVLM)
};
struct mtmd_context {
struct clip_ctx * ctx_clip;
const struct llama_model * text_model;
std::vector<float> image_embd_v; // image embedding vector
bool print_timings;
int n_threads;
std::string image_marker;
// for minicpmv, we need special tokens in-between slices
mtmd_slice_tmpl slice_tmpl = MTMD_SLICE_TMPL_NONE;
llama_token tok_ov_img_start = LLAMA_TOKEN_NULL; // overview image
llama_token tok_ov_img_end = LLAMA_TOKEN_NULL; // overview image
llama_token tok_slices_start = LLAMA_TOKEN_NULL; // start of all slices
llama_token tok_slices_end = LLAMA_TOKEN_NULL; // end of all slices
llama_token tok_sli_img_start = LLAMA_TOKEN_NULL; // single slice
llama_token tok_sli_img_end = LLAMA_TOKEN_NULL; // single slice
llama_token tok_row_end = LLAMA_TOKEN_NULL; // end of row
bool use_mrope = false; // for Qwen2VL, we need to use M-RoPE
// TODO @ngxson : add timings
mtmd_context(const char * mmproj_fname,
const llama_model * text_model,
const mtmd_context_params & ctx_params) :
text_model (text_model),
print_timings(ctx_params.print_timings),
n_threads (ctx_params.n_threads),
image_marker (ctx_params.image_marker)
{
clip_context_params ctx_clip_params;
ctx_clip_params.use_gpu = ctx_params.use_gpu;
ctx_clip_params.verbosity = ctx_params.verbosity;
ctx_clip = clip_init(mmproj_fname, ctx_clip_params);
if (!ctx_clip) {
throw std::runtime_error(string_format("Failed to load CLIP model from %s\n", mmproj_fname));
}
use_mrope = clip_is_qwen2vl(ctx_clip);
int minicpmv_version = clip_is_minicpmv(ctx_clip);
if (minicpmv_version == 2) {
// minicpmv 2.5 format:
// <image> (overview) </image><slice><image> (slice) </image><image> (slice) </image>\n ... </slice>
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_5;
tok_ov_img_start = lookup_token("<image>");
tok_ov_img_end = lookup_token("</image>");
tok_slices_start = lookup_token("<slice>");
tok_slices_end = lookup_token("</slice>");
tok_sli_img_start = tok_ov_img_start;
tok_sli_img_end = tok_ov_img_end;
tok_row_end = lookup_token("\n");
} else if (minicpmv_version == 3 || minicpmv_version == 4) {
// minicpmv 2.6 format:
// <image> (overview) </image><slice> (slice) </slice><slice> (slice) </slice>\n ...
slice_tmpl = MTMD_SLICE_TMPL_MINICPMV_2_6;
tok_ov_img_start = lookup_token("<image>");
tok_ov_img_end = lookup_token("</image>");
tok_sli_img_start = lookup_token("<slice>");
tok_sli_img_end = lookup_token("</slice>");
tok_row_end = lookup_token("\n");
} else if (minicpmv_version != 0) {
GGML_ASSERT(false && "unsupported minicpmv version");
}
}
~mtmd_context() {
clip_free(ctx_clip);
}
private:
llama_token lookup_token(const std::string & token_text) {
const llama_vocab * vocab = llama_model_get_vocab(text_model);
const int n_vocab = llama_vocab_n_tokens(vocab);
for (int i = 0; i < n_vocab; i++) {
if (token_to_piece(vocab, i, true) == token_text) {
return i;
}
}
return LLAMA_TOKEN_NULL;
}
std::string token_to_piece(const llama_vocab * vocab, llama_token token, bool special) {
std::string piece;
piece.resize(piece.capacity()); // using string internal cache, 15 bytes + '\n'
const int n_chars = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
if (n_chars < 0) {
piece.resize(-n_chars);
int check = llama_token_to_piece(vocab, token, &piece[0], piece.size(), 0, special);
GGML_ASSERT(check == -n_chars);
} else {
piece.resize(n_chars);
}
return piece;
}
};
struct mtmd_image_tokens_data {
clip_image_f32_batch batch_f32; // preprocessed image patches
};
struct mtmd_image_tokens {
uint32_t nx; // number of tokens in x direction
uint32_t ny; // number of tokens in y direction
bool use_mrope_pos = false; // use M-RoPE position counting (the whole image is 1 temporal position)
uint32_t n_tokens() const { return nx * ny; }
clip_image_f32_batch batch_f32; // preprocessed image patches
std::string id; // optional user-defined ID, useful for KV cache tracking
};
mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
const struct llama_model * text_model,
const struct mtmd_context_params ctx_params) {
try {
return new mtmd_context(mmproj_fname, text_model, ctx_params);
} catch (const std::exception & e) {
LOG_ERR("%s: error: %s\n", __func__, e.what());
return nullptr;
}
}
void mtmd_free(mtmd_context * ctx) {
if (ctx) {
delete ctx;
}
}
// copied from common_tokenize
static std::vector<llama_token> mtmd_tokenize_text_internal(
const struct llama_vocab * vocab,
const std::string & text,
bool add_special,
bool parse_special) {
// upper limit for the number of tokens
int n_tokens = text.length() + 2 * add_special;
std::vector<llama_token> result(n_tokens);
n_tokens = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
if (n_tokens < 0) {
result.resize(-n_tokens);
int check = llama_tokenize(vocab, text.data(), text.length(), result.data(), result.size(), add_special, parse_special);
GGML_ASSERT(check == -n_tokens);
} else {
result.resize(n_tokens);
}
return result;
}
int32_t mtmd_tokenize(mtmd_context * ctx,
std::vector<mtmd_input_chunk> & output,
const mtmd_input_text & text,
const std::vector<mtmd_bitmap> & bitmaps) {
auto vocab = llama_model_get_vocab(ctx->text_model);
std::string prompt_modified(text.text);
std::string marker_modified(ctx->image_marker);
projector_type proj_type = clip_get_projector_type(ctx->ctx_clip);
// a bit hacky here, but works for now
// for some models, we need to add prefix and suffix to the image embeddings
if (clip_is_gemma3(ctx->ctx_clip)) {
// gemma 3
// <start_of_image> ... (image embeddings) ... <end_of_image>
marker_modified = "<start_of_image>" + ctx->image_marker + "<end_of_image>";
string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
} else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) {
// <|begin_of_image|> ... (image embeddings) ... <|end_of_image|>
marker_modified = "<|begin_of_image|>" + ctx->image_marker + "<|end_of_image|>";
string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
} else if (proj_type == PROJECTOR_TYPE_IDEFICS3) {
// https://github.com/huggingface/transformers/blob/a42ba80fa520c784c8f11a973ca9034e5f859b79/src/transformers/models/idefics3/processing_idefics3.py#L192-L215
marker_modified = "<fake_token_around_image><global-img>" + ctx->image_marker + "<fake_token_around_image>";
string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
} else if (proj_type == PROJECTOR_TYPE_PIXTRAL) {
// https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
marker_modified = ctx->image_marker + "[IMG_END]";
string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
}
else if (proj_type == PROJECTOR_TYPE_QWEN2VL || proj_type == PROJECTOR_TYPE_QWEN25VL) {
// <|vision_start|> ... (image embeddings) ... <|vision_end|>
marker_modified = "<|vision_start|>" + ctx->image_marker + "<|vision_end|>";
string_replace_all(prompt_modified, ctx->image_marker, marker_modified);
}
// llava-1.5, llava-1.6, Yi-VL, Yi-34B, granite: don't need to add prefix and suffix
std::vector<std::string> parts = string_split_str(prompt_modified, ctx->image_marker);
output.clear();
output.reserve(parts.size());
size_t i_img = 0;
// utility for adding raw tokens
auto add_text_chunk = [&output](std::vector<llama_token> && tokens) {
mtmd_input_chunk chunk{
MTMD_INPUT_CHUNK_TYPE_TEXT,
std::move(tokens),
{},
};
output.emplace_back(std::move(chunk));
};
// utility for splitting batch of multiple images into chunks of batch having single images
auto split_batch_to_chunk = [&ctx](clip_image_f32_batch && batch_f32, const std::string & id) {
std::vector<mtmd_input_chunk> chunks;
for (auto & entry : batch_f32.entries) {
mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
image_tokens->nx = clip_n_output_tokens(ctx->ctx_clip, entry.get());
image_tokens->ny = 1;
image_tokens->batch_f32.entries.push_back(std::move(entry));
image_tokens->id = id;
mtmd_input_chunk chunk{
MTMD_INPUT_CHUNK_TYPE_IMAGE,
{},
std::move(image_tokens),
};
chunks.emplace_back(std::move(chunk));
}
return chunks;
};
for (const auto & part : parts) {
// printf("tokenizing part: %s\n", part.c_str());
bool add_bos = &parts.front() == &part;
auto tokens = mtmd_tokenize_text_internal(vocab, part, text.add_special && add_bos, text.parse_special);
if (tokens.empty()) {
continue;
}
mtmd_input_chunk chunk{
MTMD_INPUT_CHUNK_TYPE_TEXT,
std::move(tokens),
{},
};
output.emplace_back(std::move(chunk));
if (&parts.back() != &part) {
// add image token to middle of 2 parts
if (i_img >= bitmaps.size()) {
LOG_ERR("%s: error: not enough images for %d parts\n", __func__, (int)parts.size());
return 1;
}
// convert mtmd_bitmap to clip_image_u8
clip_image_u8_ptr img_u8(clip_image_u8_init());
img_u8->nx = bitmaps[i_img].nx;
img_u8->ny = bitmaps[i_img].ny;
img_u8->buf.resize(bitmaps[i_img].data.size());
std::memcpy(img_u8->buf.data(), bitmaps[i_img].data.data(), img_u8->nx * img_u8->ny * 3);
clip_image_size img_u8_size{img_u8->nx, img_u8->ny};
// preprocess image
clip_image_f32_batch batch_f32;
bool ok = clip_image_preprocess(ctx->ctx_clip, img_u8.get(), &batch_f32);
if (!ok) {
LOG_ERR("Unable to preprocess image\n");
return 2;
}
if (ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_5 || ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6) {
// split batch into chunks of single images
auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmaps[i_img].id);
GGML_ASSERT(chunks.size() > 0);
// add overview image
add_text_chunk({ctx->tok_ov_img_start});
output.emplace_back(std::move(chunks.front()));
chunks.erase(chunks.begin());
add_text_chunk({ctx->tok_ov_img_end});
// add slices
if (!chunks.empty()) {
clip_add_load_image_size(ctx->ctx_clip, &img_u8_size);
int n_col = clip_uhd_num_image_embeds_col(ctx->ctx_clip);
int n_row = (int)chunks.size() / n_col;
GGML_ASSERT(n_row * n_col == (int)chunks.size());
if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
add_text_chunk({ctx->tok_slices_start});
}
for (int y = 0; y < n_row; y++) {
for (int x = 0; x < n_col; x++) {
if (ctx->tok_sli_img_start != LLAMA_TOKEN_NULL) {
add_text_chunk({ctx->tok_sli_img_start});
}
output.emplace_back(std::move(chunks[y * n_col + x]));
if (ctx->tok_sli_img_end != LLAMA_TOKEN_NULL) {
add_text_chunk({ctx->tok_sli_img_end});
}
}
if (ctx->tok_row_end != LLAMA_TOKEN_NULL && y != n_row - 1) {
add_text_chunk({ctx->tok_row_end});
}
}
if (ctx->tok_slices_end != LLAMA_TOKEN_NULL) {
add_text_chunk({ctx->tok_slices_end});
}
}
} else {
size_t n_tokens = 0;
for (const auto & entry : batch_f32.entries) {
n_tokens += clip_n_output_tokens(ctx->ctx_clip, entry.get());
}
mtmd_image_tokens_ptr image_tokens(new mtmd_image_tokens);
if (ctx->use_mrope) {
// for Qwen2VL, we need this information for M-RoPE decoding positions
image_tokens->nx = clip_n_output_tokens_x(ctx->ctx_clip, batch_f32.entries[0].get());
image_tokens->ny = clip_n_output_tokens_y(ctx->ctx_clip, batch_f32.entries[0].get());
image_tokens->use_mrope_pos = true;
} else {
// other models, we only need the total number of tokens
image_tokens->nx = n_tokens;
image_tokens->ny = 1;
}
image_tokens->batch_f32 = std::move(batch_f32);
image_tokens->id = bitmaps[i_img].id; // optional
LOG_DBG("image_tokens->nx = %d\n", image_tokens->nx);
LOG_DBG("image_tokens->ny = %d\n", image_tokens->ny);
LOG_DBG("batch_f32 size = %d\n", (int)image_tokens->batch_f32.entries.size());
mtmd_input_chunk chunk{
MTMD_INPUT_CHUNK_TYPE_IMAGE,
{},
std::move(image_tokens),
};
output.emplace_back(std::move(chunk));
}
i_img++; // move to next image
}
}
return 0;
}
void mtmd_image_tokens_free(mtmd_image_tokens * image_tokens) {
if (image_tokens) {
delete image_tokens;
}
}
size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens) {
return image_tokens->n_tokens();
}
size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens) {
return image_tokens->nx;
}
size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens) {
return image_tokens->ny;
}
std::string mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens) {
return image_tokens->id;
}
llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens) {
if (image_tokens->use_mrope_pos) {
return 1; // for M-RoPE, the whole image is 1 in temporal dimension
}
return image_tokens->n_tokens();
}
int32_t mtmd_encode(mtmd_context * ctx, const mtmd_image_tokens * image_tokens) {
int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip);
ctx->image_embd_v.resize(image_tokens->n_tokens() * n_mmproj_embd);
bool ok = false;
// only effective for minicpmv and qwen2vl, other models will ignore load_image_size
{
clip_image_size slice_size{
image_tokens->batch_f32.entries[0]->nx,
image_tokens->batch_f32.entries[0]->ny};
clip_add_load_image_size(ctx->ctx_clip, &slice_size);
}
if (clip_is_llava(ctx->ctx_clip) || clip_is_minicpmv(ctx->ctx_clip) || clip_is_glm(ctx->ctx_clip)) {
// TODO @ngxson : llava does not support batched encoding ; this should be fixed inside clip_image_batch_encode()
const auto & entries = image_tokens->batch_f32.entries;
for (size_t i = 0; i < entries.size(); i++) {
int n_tokens_per_image = clip_n_output_tokens(ctx->ctx_clip, entries[i].get());
ok = clip_image_encode(
ctx->ctx_clip,
ctx->n_threads,
entries[i].get(),
ctx->image_embd_v.data() + i*n_mmproj_embd*n_tokens_per_image);
}
} else {
ok = clip_image_batch_encode(
ctx->ctx_clip,
ctx->n_threads,
&image_tokens->batch_f32,
ctx->image_embd_v.data());
}
return ok ? 0 : 1;
}
float * mtmd_get_output_embd(mtmd_context * ctx) {
return ctx->image_embd_v.data();
}
size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks) {
size_t n_tokens = 0;
for (auto & chunk : chunks) {
if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
n_tokens += chunk.tokens_text.size();
} else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
n_tokens += mtmd_image_tokens_get_n_tokens(chunk.tokens_image.get());
} else {
GGML_ASSERT(false && "chunk type not supported");
}
}
return n_tokens;
}
llama_pos mtmd_helper_get_n_pos(mtmd_input_chunks & chunks) {
llama_pos n_pos = 0;
for (auto & chunk : chunks) {
if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
n_pos += chunk.tokens_text.size();
} else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
n_pos += mtmd_image_tokens_get_n_pos(chunk.tokens_image.get());
} else {
GGML_ASSERT(false && "chunk type not supported");
}
}
return n_pos;
}
// helper struct to make working with embd batch easier
// note: this will be removed after llama_batch_ext refactoring
struct decode_embd_batch {
int n_pos_per_embd;
int n_mmproj_embd;
std::vector<llama_pos> pos;
std::vector<llama_pos> pos_view; // used by mrope
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id> seq_id_0;
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
pos .resize(n_tokens * n_pos_per_embd);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
logits .resize(n_tokens);
seq_id_0.resize(1);
seq_ids [n_tokens] = nullptr;
batch = {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
/*logits =*/ logits.data(),
};
}
void set_position_normal(llama_pos pos_0, llama_seq_id seq_id) {
seq_id_0[0] = seq_id;
for (int i = 0; i < batch.n_tokens; i++) {
batch.pos [i] = pos_0 + i;
batch.n_seq_id[i] = 1;
batch.seq_id [i] = seq_id_0.data();
batch.logits [i] = false;
}
}
void set_position_mrope(llama_pos pos_0, int nx, int ny, llama_seq_id seq_id) {
GGML_ASSERT(n_pos_per_embd == 4);
seq_id_0[0] = seq_id;
for (int y = 0; y < ny; y++) {
for (int x = 0; x < nx; x++) {
int i = y * nx + x;
pos[i ] = pos_0;
pos[i + batch.n_tokens ] = pos_0 + y;
pos[i + batch.n_tokens * 2] = pos_0 + x;
pos[i + batch.n_tokens * 3] = 0; // last pos dim is unused
}
}
for (int i = 0; i < batch.n_tokens; i++) {
batch.n_seq_id[i] = 1;
batch.seq_id [i] = seq_id_0.data();
batch.logits [i] = false;
}
}
llama_batch get_view(int offset, int n_tokens) {
llama_pos * pos_ptr;
pos_view.clear();
pos_view.resize(n_tokens * n_pos_per_embd);
if (n_pos_per_embd > 1) {
// mrope
// for example, with layout of src: 1234...1234...1234...1234...
// offset 2 will give us dst: 34...34...34...34...
for (int i = 0; i < n_pos_per_embd; i++) {
auto src = pos.begin() + i * batch.n_tokens + offset;
pos_view.insert(pos_view.end(), src, src + n_tokens);
}
pos_ptr = pos_view.data();
} else {
// normal
pos_ptr = pos.data() + offset;
}
return {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ batch.embd + offset * n_mmproj_embd,
/*pos =*/ pos_ptr,
/*n_seq_id =*/ batch.n_seq_id + offset,
/*seq_id =*/ batch.seq_id + offset,
/*logits =*/ batch.logits + offset,
};
}
};
int32_t mtmd_helper_eval(mtmd_context * ctx,
llama_context * lctx,
mtmd_input_chunks & chunks,
llama_pos pos0,
llama_seq_id seq_id,
int32_t n_batch) {
int32_t ret;
llama_pos n_past = pos0;
llama_batch text_batch = llama_batch_init(n_batch, 0, 1);
int n_mmproj_embd = clip_n_mmproj_embd(ctx->ctx_clip);
int n_pos_per_embd = mtmd_decode_use_mrope(ctx) ? 4 : 1;
for (auto & chunk : chunks) {
bool is_last = &chunk == &chunks.back();
if (chunk.type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
text_batch.n_tokens = chunk.tokens_text.size();
size_t i = 0;
while (i < chunk.tokens_text.size()) { // split into batches
for (; i < chunk.tokens_text.size() && text_batch.n_tokens < n_batch; i++) {
text_batch.token [i] = chunk.tokens_text[i];
text_batch.pos [i] = n_past++;
text_batch.n_seq_id[i] = 1;
text_batch.seq_id [i][0] = seq_id;
text_batch.logits [i] = false;
}
if (is_last) {
// always get logits for last input chunk
text_batch.logits[text_batch.n_tokens - 1] = true;
}
ret = llama_decode(lctx, text_batch);
if (ret != 0) {
LOG_ERR("failed to decode text\n");
llama_batch_free(text_batch);
return ret;
}
}
} else if (chunk.type == MTMD_INPUT_CHUNK_TYPE_IMAGE) {
GGML_ASSERT(!is_last && "logits for last image chunk is not yet supported");
GGML_ASSERT(chunk.tokens_image != nullptr);
int64_t t0 = ggml_time_ms();
if (ctx->print_timings) {
LOG_INF("encoding image or slice...\n");
}
ret = mtmd_encode(ctx, chunk.tokens_image.get());
if (ret != 0) {
LOG_ERR("failed to encode image\n");
llama_batch_free(text_batch);
return ret;
}
if (ctx->print_timings) {
LOG_INF("image/slice encoded in %" PRId64 " ms\n", ggml_time_ms() - t0);
}
int32_t n_tokens = mtmd_image_tokens_get_n_tokens(chunk.tokens_image.get());
int32_t i_batch = 0;
int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch;
float * embd = mtmd_get_output_embd(ctx);
decode_embd_batch batch_embd(embd, n_tokens, n_pos_per_embd, n_mmproj_embd);
const int nx = mtmd_image_tokens_get_nx(chunk.tokens_image.get());
const int ny = mtmd_image_tokens_get_ny(chunk.tokens_image.get());
if (mtmd_decode_use_mrope(ctx)) {
batch_embd.set_position_mrope(n_past, nx, ny, seq_id);
} else {
batch_embd.set_position_normal(n_past, seq_id);
}
if (mtmd_decode_use_non_causal(ctx)) {
llama_set_causal_attn(lctx, false);
// TODO @ngxson : need to make sure only one image is processed at a time, and n_ubatch must be enough to hold the image
}
while (i_batch < n_img_batches) { // split into batches
int pos_offset = i_batch*n_batch;
int n_tokens_batch = std::min(n_batch, n_tokens - pos_offset);
llama_batch batch_embd_view = batch_embd.get_view(pos_offset, n_tokens_batch);
LOG_INF("decoding image batch %d/%d, n_tokens_batch = %d\n", i_batch+1, n_img_batches, n_tokens_batch);
int64_t t1 = ggml_time_ms();
ret = llama_decode(lctx, batch_embd_view);
if (ret != 0) {
LOG_ERR("failed to decode image\n");
llama_set_causal_attn(lctx, true); // restore causal attn
llama_batch_free(text_batch);
return ret;
}
if (ctx->print_timings) {
LOG_INF("image decoded (batch %d/%d) in %" PRId64 " ms\n", i_batch+1, n_img_batches, ggml_time_ms() - t1);
}
i_batch++;
}
// for mrope, one image is one single **temporal** position
n_past += mtmd_decode_use_mrope(ctx) ? 1 : n_tokens;
if (mtmd_decode_use_non_causal(ctx)) {
llama_set_causal_attn(lctx, true);
}
} else {
GGML_ASSERT(false && "chunk type not supported");
}
}
llama_batch_free(text_batch);
return 0;
}
int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output) {
clip_image_u8_ptr img_u8(clip_image_u8_init());
bool ok = clip_image_load_from_bytes(buf, len, img_u8.get());
if (!ok) {
LOG_ERR("Unable to load image from buffer\n");
return 1;
}
unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny);
output.data.resize(output.nx * output.ny * 3);
std::memcpy(output.data.data(), data, output.nx * output.ny * 3);
return 0;
}
int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output) {
clip_image_u8_ptr img_u8(clip_image_u8_init());
bool ok = clip_image_load_from_file(fname, img_u8.get());
if (!ok) {
LOG_ERR("Unable to load image %s\n", fname);
return 1;
}
unsigned char * data = clip_image_u8_get_data(img_u8.get(), &output.nx, &output.ny);
output.data.resize(output.nx * output.ny * 3);
std::memcpy(output.data.data(), data, output.nx * output.ny * 3);
return 0;
}
bool mtmd_decode_use_non_causal(mtmd_context * ctx) {
projector_type proj_type = clip_get_projector_type(ctx->ctx_clip);
if (proj_type == PROJECTOR_TYPE_GEMMA3) {
return true;
}
return false;
}
bool mtmd_decode_use_mrope(mtmd_context * ctx) {
return ctx->use_mrope;
}
void mtmd_image_tokens_deleter::operator()(mtmd_image_tokens * val) {
mtmd_image_tokens_free(val);
}

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#ifndef MTMD_H
#define MTMD_H
#include "ggml.h"
#include "llama.h"
#include "clip.h"
#include <vector>
#include <cinttypes>
#include <memory>
#ifdef LLAMA_SHARED
# if defined(_WIN32) && !defined(__MINGW32__)
# ifdef LLAMA_BUILD
# define MTMD_API __declspec(dllexport)
# else
# define MTMD_API __declspec(dllimport)
# endif
# else
# define MTMD_API __attribute__ ((visibility ("default")))
# endif
#else
# define MTMD_API
#endif
#ifdef __cplusplus
enum mtmd_input_chunk_type {
MTMD_INPUT_CHUNK_TYPE_TEXT,
MTMD_INPUT_CHUNK_TYPE_IMAGE,
};
struct mtmd_context;
struct mtmd_image_tokens;
// represents raw image data, layout is RGBRGBRGB...
// length of data must be nx * ny * 3
struct mtmd_bitmap {
uint32_t nx;
uint32_t ny;
std::vector<unsigned char> data;
std::string id; // optional user-defined id, for ex: can be set to image hash, useful for KV cache tracking
};
struct mtmd_image_tokens_deleter {
void operator()(mtmd_image_tokens * val); // forward declaration
};
using mtmd_image_tokens_ptr = std::unique_ptr<mtmd_image_tokens, mtmd_image_tokens_deleter>;
struct mtmd_input_chunk {
mtmd_input_chunk_type type;
std::vector<llama_token> tokens_text;
mtmd_image_tokens_ptr tokens_image;
};
using mtmd_input_chunks = std::vector<mtmd_input_chunk>;
struct mtmd_context_params {
bool use_gpu = true;
bool print_timings = true;
int n_threads = 4;
enum ggml_log_level verbosity = GGML_LOG_LEVEL_INFO;
const char * image_marker = "<__image__>";
};
struct mtmd_input_text {
std::string text;
bool add_special;
bool parse_special;
};
// initialize the mtmd context
// return nullptr on failure
MTMD_API mtmd_context * mtmd_init_from_file(const char * mmproj_fname,
const llama_model * text_model,
const mtmd_context_params ctx_params);
MTMD_API void mtmd_free(mtmd_context * ctx);
// tokenize an input text prompt and an image
// the prompt must have the input image marker (default: "<__image__>") in it
// the marker will be replaced with the image tokens
// for example:
// "here is an image: <__image__>\ndescribe it in detail."
// this will gives 3 chunks:
// 1. "here is an image: <start_of_image>"
// 2. (image tokens)
// 3. "<end_of_image>\ndescribe it in detail."
// number of bitmaps must be equal to the number of image markers in the prompt
// this function is thread-safe (shared ctx)
// return values:
// 0 on success
// 1 on number of images not matching the number of markers
// 2 on image preprocessing error
MTMD_API int32_t mtmd_tokenize(mtmd_context * ctx,
std::vector<mtmd_input_chunk> & output,
const mtmd_input_text & text,
const std::vector<mtmd_bitmap> & bitmaps);
// access mtmd_image_tokens
MTMD_API size_t mtmd_image_tokens_get_n_tokens(const mtmd_image_tokens * image_tokens);
MTMD_API size_t mtmd_image_tokens_get_nx(const mtmd_image_tokens * image_tokens);
MTMD_API size_t mtmd_image_tokens_get_ny(const mtmd_image_tokens * image_tokens);
MTMD_API std::string mtmd_image_tokens_get_id(const mtmd_image_tokens * image_tokens);
MTMD_API llama_pos mtmd_image_tokens_get_n_pos(const mtmd_image_tokens * image_tokens); // number of temporal positions (always 1 for M-RoPE, n_tokens otherwise)
MTMD_API void mtmd_image_tokens_free(mtmd_image_tokens * image_tokens);
// returns 0 on success
MTMD_API int32_t mtmd_encode(mtmd_context * ctx,
const mtmd_image_tokens * image_tokens);
// get output embeddings from the last encode pass
MTMD_API float * mtmd_get_output_embd(mtmd_context * ctx);
// whether we need to set non-causal mask before llama_decode
MTMD_API bool mtmd_decode_use_non_causal(mtmd_context * ctx);
// whether the current model use M-RoPE for llama_decode
MTMD_API bool mtmd_decode_use_mrope(mtmd_context * ctx);
//
// helper functions (can be implemented based on other functions)
//
// helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache
MTMD_API size_t mtmd_helper_get_n_tokens(mtmd_input_chunks & chunks);
// helper to count the total position of tokens from a list of chunks, useful to keep track of n_past
MTMD_API llama_pos mtmd_helper_get_n_pos(mtmd_input_chunks & chunks);
// helper function that automatically:
// 1. run llama_decode() on text chunks
// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
// otherwise, returns 0 on success
MTMD_API int32_t mtmd_helper_eval(mtmd_context * ctx,
llama_context * lctx,
mtmd_input_chunks & chunks,
llama_pos pos0,
llama_seq_id seq_id,
int32_t n_batch);
// helper function to construct a mtmd_bitmap from a file
// returns 0 on success
// this function is thread-safe
MTMD_API int32_t mtmd_helper_bitmap_init_from_file(const char * fname, mtmd_bitmap & output);
// helper function to construct a mtmd_bitmap from a buffer
// the buffer must be an image in format supported by stb_image (jpg, png, bmp, gif, etc.)
// returns 0 on success
// this function is thread-safe
MTMD_API int32_t mtmd_helper_bitmap_init_from_buf(const unsigned char * buf, size_t len, mtmd_bitmap & output);
// convenient unique_ptr wrappers
struct mtmd_context_deleter {
void operator()(mtmd_context * val) { mtmd_free(val); }
};
using mtmd_context_ptr = std::unique_ptr<mtmd_context, mtmd_context_deleter>;
#else
static_assert(false && "C header is not yet supported by this library");
#endif
#endif

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#include "arg.h"
#include "base64.hpp"
#include "log.h"
#include "common.h"
#include "sampling.h"
#include "clip.h"
#include "llava.h"
#include "llama.h"
#include "ggml.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef NDEBUG
#include "ggml-alloc.h"
#include "ggml-backend.h"
#endif
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <vector>
#include <algorithm>
#include <iostream>
#include <fstream>
#include <limits>
#include <cassert>
#include <cmath>
// THIS FILE IS ONLY USED FOR TESTING THE QWEN2VL MODEL
// IT IS NOT A PRODUCTION CODE
static bool qwen2vl_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed,
int n_batch, int * n_past, int * st_pos_id, struct clip_image_size * image_size) {
int n_embd = llama_model_n_embd(llama_get_model(ctx_llama));
const int patch_size = 14 * 2;
const int ph = image_size->height / patch_size + (image_size->height % patch_size > 0);
const int pw = image_size->width / patch_size + (image_size->width % patch_size > 0);
auto img_tokens = image_embed->n_image_pos;
// llama_pos mrope_pos[img_tokens * 4];
std::vector<llama_pos> mrope_pos;
mrope_pos.resize(img_tokens * 4);
for (int y = 0; y < ph; y++)
{
for (int x = 0; x < pw; x++)
{
int i = y * pw + x;
mrope_pos[i] = *st_pos_id;
mrope_pos[i + img_tokens] = *st_pos_id + y;
mrope_pos[i + img_tokens * 2] = *st_pos_id + x;
mrope_pos[i + img_tokens * 3] = 0;
}
}
*st_pos_id += std::max(pw, ph);
int processed = 0;
std::vector<llama_pos> batch_mrope_pos;
batch_mrope_pos.resize(img_tokens * 4);
for (int i = 0; i < img_tokens; i += n_batch) {
int n_eval = img_tokens - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
// llama_pos batch_mrope_pos[n_eval * 4];
std::fill(batch_mrope_pos.begin(), batch_mrope_pos.end(), 0);
memcpy(batch_mrope_pos.data(), &mrope_pos[processed], n_eval * sizeof(llama_pos));
memcpy(&batch_mrope_pos[n_eval * 1], &mrope_pos[img_tokens * 1 + processed], n_eval * sizeof(llama_pos));
memcpy(&batch_mrope_pos[n_eval * 2], &mrope_pos[img_tokens * 2 + processed], n_eval * sizeof(llama_pos));
memcpy(&batch_mrope_pos[n_eval * 3], &mrope_pos[img_tokens * 3 + processed], n_eval * sizeof(llama_pos));
llama_batch batch = {
int32_t(n_eval), // n_tokens
nullptr, // token
(image_embed->embed+i*n_embd), // embed
batch_mrope_pos.data(), // pos
nullptr, // n_seq_id
nullptr, // seq_id
nullptr, // logits
};
if (llama_decode(ctx_llama, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
*n_past += n_eval;
processed += n_eval;
}
return true;
}
static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past, int * st_pos_id) {
int N = (int) tokens.size();
for (int i = 0; i < N; i += n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
auto batch = llama_batch_get_one(&tokens[i], n_eval);
if (llama_decode(ctx_llama, batch)) {
LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
return false;
}
*n_past += n_eval;
*st_pos_id += n_eval;
}
return true;
}
static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past, int * st_pos_id) {
std::vector<llama_token> tokens;
tokens.push_back(id);
return eval_tokens(ctx_llama, tokens, 1, n_past, st_pos_id);
}
static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, int * st_pos_id, bool add_bos){
std::string str2 = str;
std::vector<llama_token> embd_inp = common_tokenize(ctx_llama, str2, add_bos, true);
eval_tokens(ctx_llama, embd_inp, n_batch, n_past, st_pos_id);
return true;
}
static const char * sample(struct common_sampler * smpl,
struct llama_context * ctx_llama,
int * n_past, int * st_pos_id) {
const llama_token id = common_sampler_sample(smpl, ctx_llama, -1);
common_sampler_accept(smpl, id, true);
const llama_model * model = llama_get_model(ctx_llama);
const llama_vocab * vocab = llama_model_get_vocab(model);
static std::string ret;
if (llama_vocab_is_eog(vocab, id)) {
ret = "</s>";
} else {
ret = common_token_to_piece(ctx_llama, id);
}
eval_id(ctx_llama, id, n_past, st_pos_id);
return ret.c_str();
}
static const char* IMG_BASE64_TAG_BEGIN = "<img src=\"data:image/jpeg;base64,";
static const char* IMG_BASE64_TAG_END = "\">";
static void find_image_tag_in_prompt(const std::string& prompt, size_t& begin_out, size_t& end_out) {
begin_out = prompt.find(IMG_BASE64_TAG_BEGIN);
end_out = prompt.find(IMG_BASE64_TAG_END, (begin_out == std::string::npos) ? 0UL : begin_out);
}
static bool prompt_contains_image(const std::string& prompt) {
size_t begin, end;
find_image_tag_in_prompt(prompt, begin, end);
return (begin != std::string::npos);
}
// replaces the base64 image tag in the prompt with `replacement`
static llava_image_embed * llava_image_embed_make_with_prompt_base64(struct clip_ctx * ctx_clip, int n_threads, const std::string& prompt) {
size_t img_base64_str_start, img_base64_str_end;
find_image_tag_in_prompt(prompt, img_base64_str_start, img_base64_str_end);
if (img_base64_str_start == std::string::npos || img_base64_str_end == std::string::npos) {
LOG_ERR("%s: invalid base64 image tag. must be %s<base64 byte string>%s\n", __func__, IMG_BASE64_TAG_BEGIN, IMG_BASE64_TAG_END);
return NULL;
}
auto base64_bytes_start = img_base64_str_start + strlen(IMG_BASE64_TAG_BEGIN);
auto base64_bytes_count = img_base64_str_end - base64_bytes_start;
auto base64_str = prompt.substr(base64_bytes_start, base64_bytes_count );
auto required_bytes = base64::required_encode_size(base64_str.size());
auto img_bytes = std::vector<unsigned char>(required_bytes);
base64::decode(base64_str.begin(), base64_str.end(), img_bytes.begin());
auto embed = llava_image_embed_make_with_bytes(ctx_clip, n_threads, img_bytes.data(), img_bytes.size());
if (!embed) {
LOG_ERR("%s: could not load image from base64 string.\n", __func__);
return NULL;
}
return embed;
}
static std::string remove_image_from_prompt(const std::string& prompt, const char * replacement = "") {
size_t begin, end;
find_image_tag_in_prompt(prompt, begin, end);
if (begin == std::string::npos || end == std::string::npos) {
return prompt;
}
auto pre = prompt.substr(0, begin);
auto post = prompt.substr(end + strlen(IMG_BASE64_TAG_END));
return pre + replacement + post;
}
struct llava_context {
struct clip_ctx * ctx_clip = NULL;
struct llama_context * ctx_llama = NULL;
struct llama_model * model = NULL;
};
static void print_usage(int, char ** argv) {
LOG("\n example usage:\n");
LOG("\n %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> --image <path/to/another/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
LOG("\n note: a lower temperature value like 0.1 is recommended for better quality.\n");
}
static struct llava_image_embed * load_image(llava_context * ctx_llava, common_params * params, const std::string & fname) {
// load and preprocess the image
llava_image_embed * embed = NULL;
auto prompt = params->prompt;
if (prompt_contains_image(prompt)) {
if (!params->image.empty()) {
LOG_INF("using base64 encoded image instead of command line image path\n");
}
embed = llava_image_embed_make_with_prompt_base64(ctx_llava->ctx_clip, params->cpuparams.n_threads, prompt);
if (!embed) {
LOG_ERR("%s: can't load image from prompt\n", __func__);
return NULL;
}
params->prompt = remove_image_from_prompt(prompt);
} else {
embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->cpuparams.n_threads, fname.c_str());
if (!embed) {
fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str());
return NULL;
}
}
return embed;
}
static void process_prompt(struct llava_context * ctx_llava, struct llava_image_embed * image_embed, common_params * params, const std::string & prompt) {
int n_past = 0;
int cur_pos_id = 0;
const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict;
std::string system_prompt, user_prompt;
size_t image_pos = prompt.find("<|vision_start|>");
if (image_pos != std::string::npos) {
// new templating mode: Provide the full prompt including system message and use <image> as a placeholder for the image
system_prompt = prompt.substr(0, image_pos);
user_prompt = prompt.substr(image_pos + std::string("<|vision_pad|>").length());
LOG_INF("system_prompt: %s\n", system_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = common_tokenize(ctx_llava->ctx_llama, system_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
LOG_INF("user_prompt: %s\n", user_prompt.c_str());
if (params->verbose_prompt) {
auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
} else {
// llava-1.5 native mode
system_prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|>";
user_prompt = "<|vision_end|>" + prompt + "<|im_end|>\n<|im_start|>assistant\n";
if (params->verbose_prompt) {
auto tmp = common_tokenize(ctx_llava->ctx_llama, user_prompt, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx_llava->ctx_llama, tmp[i]).c_str());
}
}
}
eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, true);
if (image_embed != nullptr) {
auto image_size = clip_get_load_image_size(ctx_llava->ctx_clip);
qwen2vl_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past, &cur_pos_id, image_size);
}
eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, &cur_pos_id, false);
// generate the response
LOG("\n");
struct common_sampler * smpl = common_sampler_init(ctx_llava->model, params->sampling);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
exit(1);
}
std::string response = "";
for (int i = 0; i < max_tgt_len; i++) {
const char * tmp = sample(smpl, ctx_llava->ctx_llama, &n_past, &cur_pos_id);
response += tmp;
if (strcmp(tmp, "</s>") == 0) break;
if (strstr(tmp, "###")) break; // Yi-VL behavior
LOG("%s", tmp);
if (strstr(response.c_str(), "<|im_end|>")) break; // Yi-34B llava-1.6 - for some reason those decode not as the correct token (tokenizer works)
if (strstr(response.c_str(), "<|im_start|>")) break; // Yi-34B llava-1.6
if (strstr(response.c_str(), "USER:")) break; // mistral llava-1.6
fflush(stdout);
}
common_sampler_free(smpl);
LOG("\n");
}
static struct llama_model * llava_init(common_params * params) {
llama_backend_init();
llama_numa_init(params->numa);
llama_model_params model_params = common_model_params_to_llama(*params);
llama_model * model = llama_model_load_from_file(params->model.path.c_str(), model_params);
if (model == NULL) {
LOG_ERR("%s: unable to load model\n" , __func__);
return NULL;
}
return model;
}
static struct llava_context * llava_init_context(common_params * params, llama_model * model) {
const char * clip_path = params->mmproj.path.c_str();
auto prompt = params->prompt;
if (prompt.empty()) {
prompt = "describe the image in detail.";
}
auto ctx_clip = clip_model_load(clip_path, GGML_LOG_LEVEL_INFO);
llama_context_params ctx_params = common_context_params_to_llama(*params);
ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings
llama_context * ctx_llama = llama_init_from_model(model, ctx_params);
if (ctx_llama == NULL) {
LOG_ERR("%s: failed to create the llama_context\n" , __func__);
return NULL;
}
auto * ctx_llava = (struct llava_context *)malloc(sizeof(llava_context));
ctx_llava->ctx_llama = ctx_llama;
ctx_llava->ctx_clip = ctx_clip;
ctx_llava->model = model;
return ctx_llava;
}
static void llava_free(struct llava_context * ctx_llava) {
if (ctx_llava->ctx_clip) {
clip_free(ctx_llava->ctx_clip);
ctx_llava->ctx_clip = NULL;
}
llama_free(ctx_llava->ctx_llama);
llama_model_free(ctx_llava->model);
llama_backend_free();
}
#ifndef NDEBUG
static void debug_test_mrope_2d() {
// 1. Initialize backend
ggml_backend_t backend = NULL;
std::string backend_name = "";
// #ifdef GGML_USE_CUDA
// fprintf(stderr, "%s: using CUDA backend\n", __func__);
// backend = ggml_backend_cuda_init(0); // init device 0
// backend_name = "cuda";
// if (!backend) {
// fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
// }
// #endif
// if there aren't GPU Backends fallback to CPU backend
if (!backend) {
backend = ggml_backend_cpu_init();
backend_name = "cpu";
}
// Calculate the size needed to allocate
size_t ctx_size = 0;
ctx_size += 2 * ggml_tensor_overhead(); // tensors
// no need to allocate anything else!
// 2. Allocate `ggml_context` to store tensor data
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_backend_alloc_ctx_tensors()
};
struct ggml_context * ctx = ggml_init(params);
struct ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 128, 12, 30);
ggml_set_name(inp_raw, "inp_raw");
ggml_set_input(inp_raw);
struct ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 30 * 4);
ggml_set_name(pos, "pos");
ggml_set_input(pos);
std::vector<float> dummy_q;
dummy_q.resize(128 * 12 * 30);
std::fill(dummy_q.begin(), dummy_q.end(), 0.1);
// memcpy(inp_raw->data, dummy_q.data(), 128 * 12 * 30 * ggml_element_size(inp_raw));
std::vector<int> pos_id;
pos_id.resize(30 * 4);
for (int i = 0; i < 30; i ++) {
pos_id[i] = i;
pos_id[i + 30] = i + 10;
pos_id[i + 60] = i + 20;
pos_id[i + 90] = i + 30;
}
int sections[4] = {32, 32, 0, 0};
// 4. Allocate a `ggml_backend_buffer` to store all tensors
ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
// 5. Copy tensor data from main memory (RAM) to backend buffer
ggml_backend_tensor_set(inp_raw, dummy_q.data(), 0, ggml_nbytes(inp_raw));
ggml_backend_tensor_set(pos, pos_id.data(), 0, ggml_nbytes(pos));
// 6. Create a `ggml_cgraph` for mul_mat operation
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx_cgraph = NULL;
// create a temporally context to build the graph
struct ggml_init_params params0 = {
/*.mem_size =*/ ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
ctx_cgraph = ggml_init(params0);
gf = ggml_new_graph(ctx_cgraph);
struct ggml_tensor * result0 = ggml_rope_multi(
ctx_cgraph, inp_raw, pos, nullptr,
128/2, sections, LLAMA_ROPE_TYPE_VISION, 32768, 1000000, 1,
0, 1, 32, 1);
// Add "result" tensor and all of its dependencies to the cgraph
ggml_build_forward_expand(gf, result0);
// 7. Create a `ggml_gallocr` for cgraph computation
ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
ggml_gallocr_alloc_graph(allocr, gf);
// 9. Run the computation
int n_threads = 1; // Optional: number of threads to perform some operations with multi-threading
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, n_threads);
}
ggml_backend_graph_compute(backend, gf);
// 10. Retrieve results (output tensors)
// in this example, output tensor is always the last tensor in the graph
struct ggml_tensor * result = result0;
// struct ggml_tensor * result = gf->nodes[gf->n_nodes - 1];
float * result_data = (float *)malloc(ggml_nbytes(result));
// because the tensor data is stored in device buffer, we need to copy it back to RAM
ggml_backend_tensor_get(result, result_data, 0, ggml_nbytes(result));
const std::string bin_file = "mrope_2d_" + backend_name +".bin";
std::ofstream outFile(bin_file, std::ios::binary);
if (outFile.is_open()) {
outFile.write(reinterpret_cast<const char*>(result_data), ggml_nbytes(result));
outFile.close();
std::cout << "Data successfully written to " + bin_file << std::endl;
} else {
std::cerr << "Error opening file!" << std::endl;
}
free(result_data);
// 11. Free memory and exit
ggml_free(ctx_cgraph);
ggml_gallocr_free(allocr);
ggml_free(ctx);
ggml_backend_buffer_free(buffer);
ggml_backend_free(backend);
}
enum model_output_type {
conv3d,
patch_embed,
patch_win_attn_scatter,
first_attn_layer,
last_attn_layer,
attn_softmax,
final_layer,
};
static void debug_dump_img_embed(struct llava_context * ctx_llava, model_output_type output_type) {
constexpr int ih = 140;
constexpr int iw = 196;
// constexpr int ih = 56;
// constexpr int iw = 56;
// int n_embd = llama_model_n_embd(llama_get_model(ctx_llava->ctx_llama));
int n_embd = 1280;
int merge = 1;
if (output_type == model_output_type::final_layer) {
n_embd = 2048;
merge = 2;
}
else if (output_type == model_output_type::attn_softmax) {
merge = 1;
n_embd = (ih/14/merge) * (iw/14/merge) * 16;
}
int ne = (ih/14/merge) * (iw/14/merge) * n_embd;
float vals[iw * ih * 3];
// float embd[ne];
std::vector<float> embd;
embd.resize(ne);
for (int i = 0; i < iw*ih; i++)
{
for (int c = 0; c < 3; c++)
vals[i * 3 + c] = (float)i / (iw*ih);
}
clip_encode_float_image(ctx_llava->ctx_clip, 8, vals, ih, iw, embd.data());
std::string file_postfix = "";
switch (output_type)
{
case model_output_type::conv3d:
file_postfix = "conv3d";
break;
case model_output_type::patch_embed:
file_postfix = "patch_embed";
break;
case model_output_type::patch_win_attn_scatter:
file_postfix = "scatter";
break;
case model_output_type::first_attn_layer:
file_postfix = "first_attn";
break;
case model_output_type::last_attn_layer:
file_postfix = "last_attn";
break;
case model_output_type::attn_softmax:
file_postfix = "attn_softmax";
break;
case model_output_type::final_layer:
file_postfix = "final";
break;
default:
break;
}
auto output_path = "img_embed_" + file_postfix + ".bin";
std::ofstream outFile(output_path, std::ios::binary);
if (outFile.is_open()) {
outFile.write(reinterpret_cast<const char*>(embd.data()), ne * sizeof(float));
outFile.close();
std::cout << "Data successfully written to ::[ " << output_path << std::endl;
} else {
std::cerr << "Error opening file!" << std::endl;
}
}
#endif
int main(int argc, char ** argv) {
ggml_time_init();
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LLAVA, print_usage)) {
return 1;
}
common_init();
if (params.mmproj.path.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) {
print_usage(argc, argv);
return 1;
}
auto * model = llava_init(&params);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to init llava model\n", __func__);
return 1;
}
if (prompt_contains_image(params.prompt)) {
auto * ctx_llava = llava_init_context(&params, model);
auto * image_embed = load_image(ctx_llava, &params, "");
// process the prompt
process_prompt(ctx_llava, image_embed, &params, params.prompt);
llama_perf_context_print(ctx_llava->ctx_llama);
llava_image_embed_free(image_embed);
ctx_llava->model = NULL;
llava_free(ctx_llava);
#ifndef NDEBUG
} else if (params.image[0].empty()) {
auto ctx_llava = llava_init_context(&params, model);
// debug_test_mrope_2d();
debug_dump_img_embed(ctx_llava, model_output_type::final_layer);
// debug_dump_img_embed(ctx_llava, model_output_type::last_attn_layer);
llama_perf_context_print(ctx_llava->ctx_llama);
ctx_llava->model = NULL;
llava_free(ctx_llava);
#endif
} else {
for (auto & image : params.image) {
auto * ctx_llava = llava_init_context(&params, model);
auto * image_embed = load_image(ctx_llava, &params, image);
if (!image_embed) {
LOG_ERR("%s: failed to load image %s. Terminating\n\n", __func__, image.c_str());
return 1;
}
// process the prompt
process_prompt(ctx_llava, image_embed, &params, params.prompt);
llama_perf_context_print(ctx_llava->ctx_llama);
llava_image_embed_free(image_embed);
ctx_llava->model = NULL;
llava_free(ctx_llava);
}
}
llama_model_free(model);
return 0;
}

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-r ../../requirements/requirements-convert_legacy_llama.txt
--extra-index-url https://download.pytorch.org/whl/cpu
pillow~=10.2.0
torch~=2.2.1
torchvision~=0.17.1

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#!/bin/bash
# make sure we are in the right directory
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR
#export LLAMA_CACHE="$SCRIPT_DIR/tmp"
set -eux
mkdir -p $SCRIPT_DIR/output
PROJ_ROOT="$SCRIPT_DIR/../.."
cd $PROJ_ROOT
# Check if the first argument is "big", then run test with big models
# This is useful if we're running the script on a larger machine, so we can test the big models
RUN_BIG_TESTS=false
if [ "${1:-}" = "big" ]; then
RUN_BIG_TESTS=true
echo "Include BIG models..."
fi
###############
arr_bin=()
arr_hf=()
arr_tmpl=() # chat template
add_test() {
local bin=$1
local hf=$2
local tmpl=${3:-""} # default to empty string if not provided
arr_bin+=("$bin")
arr_hf+=("$hf")
arr_tmpl+=("$tmpl")
}
add_test "llama-mtmd-cli" "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
add_test "llama-mtmd-cli" "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
add_test "llama-mtmd-cli" "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
add_test "llama-mtmd-cli" "second-state/Llava-v1.5-7B-GGUF:Q2_K" "vicuna"
add_test "llama-mtmd-cli" "cjpais/llava-1.6-mistral-7b-gguf:Q3_K" "vicuna"
add_test "llama-mtmd-cli" "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted
add_test "llama-mtmd-cli" "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
add_test "llama-mtmd-cli" "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
add_test "llama-mtmd-cli" "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
# to test the big models, run: ./tests.sh big
if [ "$RUN_BIG_TESTS" = true ]; then
add_test "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7"
add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
# add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
# add_test "llama-mtmd-cli" "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M" # too big
fi
# these models always give the wrong answer, not sure why
# add_test "llama-mtmd-cli" "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M"
# add_test "llama-mtmd-cli" "ggml-org/SmolVLM-256M-Instruct-GGUF:Q8_0"
# add_test "llama-mtmd-cli" "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q8_0"
# this model has broken chat template, not usable
# add_test "llama-mtmd-cli" "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
###############
cmake --build build -j --target "${arr_bin[@]}"
arr_res=()
for i in "${!arr_bin[@]}"; do
bin="${arr_bin[$i]}"
hf="${arr_hf[$i]}"
tmpl="${arr_tmpl[$i]}"
echo "Running test with binary: $bin and HF model: $hf"
echo ""
echo ""
output=$(\
"$PROJ_ROOT/build/bin/$bin" \
-hf "$hf" \
--image $SCRIPT_DIR/test-1.jpeg \
-p "what is the publisher name of the newspaper?" \
--temp 0 -n 128 \
${tmpl:+--chat-template "$tmpl"} \
2>&1 | tee /dev/tty)
echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log
if echo "$output" | grep -iq "new york"; then
result="\033[32mOK\033[0m: $bin $hf"
else
result="\033[31mFAIL\033[0m: $bin $hf"
fi
echo -e "$result"
arr_res+=("$result")
echo ""
echo ""
echo ""
echo "#################################################"
echo "#################################################"
echo ""
echo ""
done
set +x
for i in "${!arr_res[@]}"; do
echo -e "${arr_res[$i]}"
done
echo ""
echo "Output logs are saved in $SCRIPT_DIR/output"

5
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set(TARGET llama-cli)
add_executable(${TARGET} main.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# llama.cpp/tools/main
This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.
## Table of Contents
1. [Quick Start](#quick-start)
2. [Common Options](#common-options)
3. [Input Prompts](#input-prompts)
4. [Interaction](#interaction)
5. [Context Management](#context-management)
6. [Generation Flags](#generation-flags)
7. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options)
8. [Additional Options](#additional-options)
## Quick Start
To get started right away, run the following command, making sure to use the correct path for the model you have:
First, we will need to download a model. In these examples, we will use the Gemma model from the ggml-org repo on Hugging Face.
[https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)
Once downloaded, place your model in the models folder in llama.cpp.
### Unix-based systems (Linux, macOS, etc.):
##### Input prompt (One-and-done)
```bash
./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
```
##### Conversation mode (Allow for continuous interaction with the model)
```bash
./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
```
##### Conversation mode using built-in jinja chat template
```bash
./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja
```
##### One-and-done query using jinja with custom system prompt and a starting prompt
```bash
./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
```
##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
```bash
./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
```
### Windows:
##### Input prompt (One-and-done)
```powershell
./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
```
##### Conversation mode (Allow for continuous interaction with the model)
```powershell
./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
```
##### Conversation mode using built-in jinja chat template
```powershell
./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja
```
##### One-and-done query using jinja with custom system prompt and a starting prompt
```powershell
./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
```
#### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
```powershell
llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
```
## Common Options
In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models:
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set).
- `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)).
- `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses.
- `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text.
- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 4096, but if a LLaMA model was built with a longer context, increasing this value will provide better results for longer input/inference.
- `-mli, --multiline-input`: Allows you to write or paste multiple lines without ending each in '\'
- `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has.
- `-ngl N, --n-gpu-layers N`: When compiled with GPU support, this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
## Input Prompts
The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts:
- `--prompt PROMPT`: Provide a prompt directly as a command-line option.
- `--file FNAME`: Provide a file containing a prompt or multiple prompts.
- `--system-prompt PROMPT`: Provide a system prompt (will otherwise use the default one in the chat template (if provided)).
- `--system-prompt-file FNAME`: Provide a file containing a system prompt.
- `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.)
## Interaction
The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.
In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing.
### Interaction Options
- `-i, --interactive`: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model.
- `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation.
- `-cnv, --conversation`: Run the program in conversation mode (does not print special tokens and suffix/prefix, use default or provided chat template) (default: true if chat template found)
- `-no-cnv`: Disable conversation mode (default: false)
- `-st, --single-turn`: Only process a single conversation turn (user input) and then exit.
- `--jinja`: Enable jinja chat template parser, will use the model's built-in template or a user-provided one (default: false)
- `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text.
By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs.
### Reverse Prompts
Reverse prompts are a powerful way to create a chat-like experience with a LLaMA model by pausing the text generation when specific text strings are encountered:
- `-r PROMPT, --reverse-prompt PROMPT`: Specify one or multiple reverse prompts to pause text generation and switch to interactive mode. For example, `-r "User:"` can be used to jump back into the conversation whenever it's the user's turn to speak. This helps create a more interactive and conversational experience. However, the reverse prompt doesn't work when it ends with a space.
To overcome this limitation, you can use the `--in-prefix` flag to add a space or any other characters after the reverse prompt.
### In-Prefix
The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag:
```sh
./llama-cli -r "User:" --in-prefix " "
```
### In-Suffix
The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag:
```sh
./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:"
```
When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled
### Chat templates
`--chat-template JINJA_TEMPLATE`: This option sets a custom jinja chat template. It accepts a string, not a file name. Default: template taken from model's metadata. Llama.cpp only supports [some pre-defined templates](https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template). These include llama2, llama3, gemma, monarch, chatml, orion, vicuna, vicuna-orca, deepseek, command-r, zephyr. When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled.
Example usage: `--chat-template gemma`
`--chat-template-file FNAME`: Load a custom jinja chat template from an external file, useful if the model contains outdated or incompatible template, some examples can be found in models/templates. Up-to-date chat templates can be downloaded from Hugging Face using scripts/get_chat_template.py
## Context Management
During text generation, LLaMA models have a limited context size, which means they can only consider a certain number of tokens from the input and generated text. When the context fills up, the model resets internally, potentially losing some information from the beginning of the conversation or instructions. Context management options help maintain continuity and coherence in these situations.
### Context Size
- `-c N, --ctx-size N`: Set the size of the prompt context (default: 4096, 0 = loaded from model). If a LLaMA model was built with a longer context, increasing this value will yield the best results on longer input/inference.
### Extended Context Size
Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model has a context length (max sequence length) of 4096 (4k) and the fine-tuned model has 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.
### Keep Prompt
The `--keep` option allows users to retain the original prompt when the model runs out of context, ensuring a connection to the initial instruction or conversation topic is maintained.
- `--keep N`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
By utilizing context management options like `--ctx-size` and `--keep`, you can maintain a more coherent and consistent interaction with the LLaMA models, ensuring that the generated text remains relevant to the original prompt or conversation.
## Generation Flags
The following options allow you to control the text generation process and fine-tune the diversity, creativity, and quality of the generated text according to your needs. By adjusting these options and experimenting with different combinations of values, you can find the best settings for your specific use case.
### Number of Tokens to Predict
- `-n N, --predict N`: Set the number of tokens to predict when generating text (default: -1, -1 = infinity, -2 = until context filled)
The `--predict` option controls the number of tokens the model generates in response to the input prompt. By adjusting this value, you can influence the length of the generated text. A higher value will result in longer text, while a lower value will produce shorter text.
A value of -1 will enable infinite text generation, even though we have a finite context window. When the context window is full, some of the earlier tokens (half of the tokens after `--keep`) will be discarded. The context must then be re-evaluated before generation can resume. On large models and/or large context windows, this will result in a significant pause in output.
If the pause is undesirable, a value of -2 will stop generation immediately when the context is filled.
The `--no-context-shift` option allows you to stop the infinite text generation once the finite context window is full.
It is important to note that the generated text may be shorter than the specified number of tokens if an End-of-Sequence (EOS) token or a reverse prompt is encountered. In interactive mode, text generation will pause and control will be returned to the user. In non-interactive mode, the program will end. In both cases, the text generation may stop before reaching the specified `--predict` value. If you want the model to keep going without ever producing End-of-Sequence on its own, you can use the `--ignore-eos` parameter.
### Temperature
- `--temp N`: Adjust the randomness of the generated text (default: 0.8).
Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.
Example usage: `--temp 0`
### Repeat Penalty
- `--repeat-penalty N`: Control the repetition of token sequences in the generated text default: 1.0, 1.0 = disabled).
- `--repeat-last-n N`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
The `repeat-penalty` option helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. The default value is 1.
The `repeat-last-n` option controls the number of tokens in the history to consider for penalizing repetition. A larger value will look further back in the generated text to prevent repetitions, while a smaller value will only consider recent tokens. A value of 0 disables the penalty, and a value of -1 sets the number of tokens considered equal to the context size (`ctx-size`).
### DRY Repetition Penalty
DRY (Don't Repeat Yourself) sampling is an effective technique for reducing repetition in generated text even across long contexts by penalizing tokens based on their recent usage patterns (original [PR link](https://github.com/oobabooga/text-generation-webui/pull/5677)).
- `--dry-multiplier N`: Set the DRY sampling multiplier (default: 0.0, 0.0 = disabled).
- `--dry-base N`: Set the DRY sampling base value (default: 1.75).
- `--dry-allowed-length N`: Set the allowed length for DRY sampling (default: 2).
- `--dry-penalty-last-n N`: Set DRY penalty for the last n tokens (default: -1, 0 = disable, -1 = context size).
- `--dry-sequence-breaker STRING`: Add a sequence breaker for DRY sampling. Can be used more than once to add multiple sequence breakers. Using this clears out the default breakers, which consist of: `['\n', ':', '"', '*']`. If the string `"none"` is supplied, no sequence breakers are used.
The `dry-multiplier` option controls the strength of the DRY sampling effect. A value of 0.0 disables DRY sampling, while higher values increase its influence. A typical recommended value is 0.8.
The `dry-base` option sets the base value for the exponential penalty calculation in DRY sampling. Higher values lead to more aggressive penalization of repetitions.
The `dry-allowed-length` option sets the maximum length of repeated sequences that will not be penalized. Repetitions shorter than or equal to this length are not penalized, allowing for natural repetitions of short phrases or common words.
The `dry-penalty-last-n` option controls how many recent tokens to consider when applying the DRY penalty. A value of -1 considers the entire context. Use a positive value to limit the consideration to a specific number of recent tokens.
The `dry-sequence-breaker` option adds a single sequence breaker and can be used more than once to specify multiple sequence breakers. Sequence breakers interrupt sequence matching and break the input into parts where matching can be applied.
DRY sampling provides more nuanced control over text generation, particularly for reducing long-range repetitions and maintaining global coherence.
Example usage: `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --dry-penalty-last-n -1 --dry-sequence-breaker "—" --dry-sequence-breaker "##"`
### Top-K Sampling
- `--top-k N`: Limit the next token selection to the K most probable tokens (default: 40).
Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top-k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text. The default value is 40.
Example usage: `--top-k 30`
### Top-P Sampling
- `--top-p N`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top-p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. The default value is 0.9.
Example usage: `--top-p 0.95`
### Min-P Sampling
- `--min-p N`: Sets a minimum base probability threshold for token selection (default: 0.1).
The Min-P sampling method was designed as an alternative to Top-P, and aims to ensure a balance of quality and variety. The parameter *p* represents the minimum probability for a token to be considered, relative to the probability of the most likely token. For example, with *p*=0.05 and the most likely token having a probability of 0.9, logits with a value less than 0.045 are filtered out.
Example usage: `--min-p 0.05`
### Locally Typical Sampling
- `--typical N`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
Locally typical sampling promotes the generation of contextually coherent and diverse text by sampling tokens that are typical or expected based on the surrounding context. By setting the parameter p between 0 and 1, you can control the balance between producing text that is locally coherent and diverse. A value closer to 1 will promote more contextually coherent tokens, while a value closer to 0 will promote more diverse tokens. A value equal to 1 disables locally typical sampling.
Example usage: `--typical 0.9`
### Mirostat Sampling
- `--mirostat N`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
- `--mirostat-lr N`: Set the Mirostat learning rate, parameter eta (default: 0.1).
- `--mirostat-ent N`: Set the Mirostat target entropy, parameter tau (default: 5.0).
Mirostat is an algorithm that actively maintains the quality of generated text within a desired range during text generation. It aims to strike a balance between coherence and diversity, avoiding low-quality output caused by excessive repetition (boredom traps) or incoherence (confusion traps).
The `--mirostat-lr` option sets the Mirostat learning rate (eta). The learning rate influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. The default value is `0.1`.
The `--mirostat-ent` option sets the Mirostat target entropy (tau), which represents the desired perplexity value for the generated text. Adjusting the target entropy allows you to control the balance between coherence and diversity in the generated text. A lower value will result in more focused and coherent text, while a higher value will lead to more diverse and potentially less coherent text. The default value is `5.0`.
Example usage: `--mirostat 2 --mirostat-lr 0.05 --mirostat-ent 3.0`
### XTC Sampling
- `--xtc-probability N`: Sets the chance for token removal (checked once on sampler start) (default: 0.0).
- `--xtc-threshold N`: Sets a minimum probability threshold for tokens to be removed (default: 0.1).
Exclude Top Choices (XTC) is a unique sampler that is designed to remove top tokens from consideration and avoid more obvious and repetitive outputs. With a chance of `xtc-probability` it searches for tokens with probabilities of `xtc-threshold` and above, then removes all such tokens except the least probable one.
By removing top tokens XTC can improve the variety of answers, break writing clichés and inhibit repition, since clichés and repeated phrases are usually more likely to appear. By keeping the last token above the threshold, XTC ensures that the answer is still coherent. XTC is meant to be used for creative tasks, but feel free to experiment with different settings for different models.
Being experimental and unique, XTC is disabled by default. The recommended combination of samplers is Min-P followed by XTC on its default settings: `--sampling-seq mx --min-p 0.02 --xtc-probability 0.5`.
Example usage: `--xtc-probability 0.5 --xtc-threshold 0.1`
### Top-nσ Sampling
- `--top-nsigma N`: Limit the next token selection to a subset of tokens with pre-softmax logits that are within n * σ less than the max logit (default: -1, -1 = disabled).
Top-nσ sampling is a text generation method that selects tokens based on a statistical threshold in pre-softmax logits. It works by only sampling from tokens with logits that are within n * σ of the maximum logit. This method helps maintain a stable sampling space regardless of temperature scaling, allowing it to perform well on reasoning tasks even in high temperatures. Without complex probability manipulation, it efficiently filters tokens directly on the pre-softmax logits. A higher value for top-nsigma (e.g., 5) will take more noisy tokens into consideration, while a lower value (e.g., 1) will focous on the more informative region of the sampling space.
Example usage: `--top-nsigma 1`
### Logit Bias
- `-l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS`: Modify the likelihood of a token appearing in the generated text completion.
The logit bias option allows you to manually adjust the likelihood of specific tokens appearing in the generated text. By providing a token ID and a positive or negative bias value, you can increase or decrease the probability of that token being generated.
For example, use `--logit-bias 15043+1` to increase the likelihood of the token 'Hello', or `--logit-bias 15043-1` to decrease its likelihood. Using a value of negative infinity, `--logit-bias 15043-inf` ensures that the token `Hello` is never produced.
A more practical use case might be to prevent the generation of `\code{begin}` and `\code{end}` by setting the `\` token (29905) to negative infinity with `-l 29905-inf`. (This is due to the prevalence of LaTeX codes that show up in LLaMA model inference.)
Example usage: `--logit-bias 29905-inf`
### RNG Seed
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.
## Performance Tuning and Memory Options
These options help improve the performance and memory usage of the LLaMA models. By adjusting these settings, you can fine-tune the model's behavior to better suit your system's capabilities and achieve optimal performance for your specific use case.
### Number of Threads
- `-t N, --threads N`: Set the number of threads to use during generation. For optimal performance, it is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Using the correct number of threads can greatly improve performance.
- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. In some systems, it is beneficial to use a higher number of threads during batch processing than during generation. If not specified, the number of threads used for batch processing will be the same as the number of threads used for generation.
### Mlock
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. This can improve performance but trades away some of the advantages of memory-mapping by requiring more RAM to run and potentially slowing down load times as the model loads into RAM.
### No Memory Mapping
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
### NUMA support
- `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes.
- `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node.
- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus.
These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root.
### Batch Size
- `-ub N`, `--ubatch-size N`: Physical batch size. This is the maximum number of tokens that may be processed at a time. Increasing this value may improve performance during prompt processing, at the expense of higher memory usage. Default: `512`.
- `-b N`, `--batch-size N`: Logical batch size. Increasing this value above the value of the physical batch size may improve prompt processing performance when using multiple GPUs with pipeline parallelism. Default: `2048`.
### Prompt Caching
- `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.
### Grammars & JSON schemas
- `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax.
- `--json-schema SCHEMA`: Specify a [JSON schema](https://json-schema.org/) to constrain model output to (e.g. `{}` for any JSON object, or `{"items": {"type": "string", "minLength": 10, "maxLength": 100}, "minItems": 10}` for a JSON array of strings with size constraints). If a schema uses external `$ref`s, you should use `--grammar "$( python examples/json_schema_to_grammar.py myschema.json )"` instead.
### Quantization
For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-and-quantize).
## LoRA (Low-Rank Adaptation) adapters
- `--lora FNAME`: Optional path to a LoRA adapter to use with scaling of 1.0. Can be mixed with `--lora-scaled` and can be repeated to use multiple adapters.
- `--lora-scaled FNAME`: Optional path to a LoRA adapter with user-defined scaling. Can be mixed with `--lora` and can repeated to use multiple adapters.
You can add LoRA adapters using `--lora` or `--lora-scaled`. For example: `--lora my_adapter_1.gguf --lora my_adapter_2.gguf ...` or `--lora-scaled lora_task_A.gguf 0.5 --lora-scaled lora_task_B.gguf 0.5`.
LoRA adapters should be in GGUF format. To convert from Hugging Face format use the `convert-lora-to-gguf.py` script. LoRA adapters are loaded separately and applied during inference - they are not merged with the main model. This means that mmap model loading is fully supported when using LoRA adapters. The old `--lora-base` flag has been removed now that merging is no longer performed.
## Additional Options
These options provide extra functionality and customization when running the LLaMA models:
- `-h, --help`: Display a help message showing all available options and their default values. This is particularly useful for checking the latest options and default values, as they can change frequently, and the information in this document may become outdated.
- `--verbose-prompt`: Print the prompt before generating text.
- `--no-display-prompt`: Don't print prompt at generation.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance.
- `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache.

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#include "arg.h"
#include "common.h"
#include "console.h"
#include "log.h"
#include "sampling.h"
#include "llama.h"
#include "chat.h"
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <signal.h>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static llama_context ** g_ctx;
static llama_model ** g_model;
static common_sampler ** g_smpl;
static common_params * g_params;
static std::vector<llama_token> * g_input_tokens;
static std::ostringstream * g_output_ss;
static std::vector<llama_token> * g_output_tokens;
static bool is_interacting = false;
static bool need_insert_eot = false;
static void print_usage(int argc, char ** argv) {
(void) argc;
LOG("\nexample usage:\n");
LOG("\n text generation: %s -m your_model.gguf -p \"I believe the meaning of life is\" -n 128 -no-cnv\n", argv[0]);
LOG("\n chat (conversation): %s -m your_model.gguf -sys \"You are a helpful assistant\"\n", argv[0]);
LOG("\n");
}
static bool file_exists(const std::string & path) {
std::ifstream f(path.c_str());
return f.good();
}
static bool file_is_empty(const std::string & path) {
std::ifstream f;
f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
return f.tellg() == 0;
}
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
static void sigint_handler(int signo) {
if (signo == SIGINT) {
if (!is_interacting && g_params->interactive) {
is_interacting = true;
need_insert_eot = true;
} else {
console::cleanup();
LOG("\n");
common_perf_print(*g_ctx, *g_smpl);
// make sure all logs are flushed
LOG("Interrupted by user\n");
common_log_pause(common_log_main());
_exit(130);
}
}
}
#endif
int main(int argc, char ** argv) {
common_params params;
g_params = &params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
return 1;
}
common_init();
auto & sparams = params.sampling;
// save choice to use color for later
// (note for later: this is a slightly awkward choice)
console::init(params.simple_io, params.use_color);
atexit([]() { console::cleanup(); });
if (params.logits_all) {
LOG_ERR("************\n");
LOG_ERR("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
LOG_ERR("************\n\n");
return 0;
}
if (params.embedding) {
LOG_ERR("************\n");
LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
LOG_ERR("************\n\n");
return 0;
}
if (params.n_ctx != 0 && params.n_ctx < 8) {
LOG_WRN("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
if (params.rope_freq_base != 0.0) {
LOG_WRN("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
}
if (params.rope_freq_scale != 0.0) {
LOG_WRN("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
}
LOG_INF("%s: llama backend init\n", __func__);
llama_backend_init();
llama_numa_init(params.numa);
llama_model * model = nullptr;
llama_context * ctx = nullptr;
common_sampler * smpl = nullptr;
g_model = &model;
g_ctx = &ctx;
g_smpl = &smpl;
std::vector<common_chat_msg> chat_msgs;
// load the model and apply lora adapter, if any
LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
common_init_result llama_init = common_init_from_params(params);
model = llama_init.model.get();
ctx = llama_init.context.get();
if (model == NULL) {
LOG_ERR("%s: error: unable to load model\n", __func__);
return 1;
}
const llama_vocab * vocab = llama_model_get_vocab(model);
auto chat_templates = common_chat_templates_init(model, params.chat_template);
LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
auto * reg = ggml_backend_dev_backend_reg(ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU));
auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_new");
auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_free");
struct ggml_threadpool_params tpp_batch =
ggml_threadpool_params_from_cpu_params(params.cpuparams_batch);
struct ggml_threadpool_params tpp =
ggml_threadpool_params_from_cpu_params(params.cpuparams);
set_process_priority(params.cpuparams.priority);
struct ggml_threadpool * threadpool_batch = NULL;
if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
threadpool_batch = ggml_threadpool_new_fn(&tpp_batch);
if (!threadpool_batch) {
LOG_ERR("%s: batch threadpool create failed : n_threads %d\n", __func__, tpp_batch.n_threads);
return 1;
}
// Start the non-batch threadpool in the paused state
tpp.paused = true;
}
struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp);
if (!threadpool) {
LOG_ERR("%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
return 1;
}
llama_attach_threadpool(ctx, threadpool, threadpool_batch);
const int n_ctx_train = llama_model_n_ctx_train(model);
const int n_ctx = llama_n_ctx(ctx);
if (n_ctx > n_ctx_train) {
LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx);
}
// auto enable conversation mode if chat template is available
const bool has_chat_template = common_chat_templates_was_explicit(chat_templates.get());
if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) {
if (has_chat_template) {
LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__);
params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
} else {
params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
}
}
// in case user force-activate conversation mode (via -cnv) without proper chat template, we show a warning
if (params.conversation_mode && !has_chat_template) {
LOG_WRN("%s: chat template is not available or is not supported. This may cause the model to output suboptimal responses\n", __func__);
}
// print chat template example in conversation mode
if (params.conversation_mode) {
if (params.enable_chat_template) {
if (!params.prompt.empty() && params.system_prompt.empty()) {
LOG_WRN("*** User-specified prompt will pre-start conversation, did you mean to set --system-prompt (-sys) instead?\n");
}
LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(chat_templates.get(), params.use_jinja).c_str());
} else {
LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
}
}
// print system information
{
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
LOG_INF("\n");
}
std::string path_session = params.path_prompt_cache;
std::vector<llama_token> session_tokens;
if (!path_session.empty()) {
LOG_INF("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
if (!file_exists(path_session)) {
LOG_INF("%s: session file does not exist, will create.\n", __func__);
} else if (file_is_empty(path_session)) {
LOG_INF("%s: The session file is empty. A new session will be initialized.\n", __func__);
} else {
// The file exists and is not empty
session_tokens.resize(n_ctx);
size_t n_token_count_out = 0;
if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
LOG_ERR("%s: failed to load session file '%s'\n", __func__, path_session.c_str());
return 1;
}
session_tokens.resize(n_token_count_out);
LOG_INF("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
}
}
const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja;
if (!llama_model_has_encoder(model)) {
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
}
LOG_DBG("n_ctx: %d, add_bos: %d\n", n_ctx, add_bos);
std::vector<llama_token> embd_inp;
bool waiting_for_first_input = false;
auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
common_chat_msg new_msg;
new_msg.role = role;
new_msg.content = content;
auto formatted = common_chat_format_single(chat_templates.get(), chat_msgs, new_msg, role == "user", g_params->use_jinja);
chat_msgs.push_back(new_msg);
LOG_DBG("formatted: '%s'\n", formatted.c_str());
return formatted;
};
std::string prompt;
{
if (params.conversation_mode && params.enable_chat_template) {
if (!params.system_prompt.empty()) {
// format the system prompt (will use template default if empty)
chat_add_and_format("system", params.system_prompt);
}
if (!params.prompt.empty()) {
// format and append the user prompt
chat_add_and_format("user", params.prompt);
} else {
waiting_for_first_input = true;
}
if (!params.system_prompt.empty() || !params.prompt.empty()) {
common_chat_templates_inputs inputs;
inputs.messages = chat_msgs;
inputs.add_generation_prompt = !params.prompt.empty();
prompt = common_chat_templates_apply(chat_templates.get(), inputs).prompt;
}
} else {
// otherwise use the prompt as is
prompt = params.prompt;
}
if (params.interactive_first || !prompt.empty() || session_tokens.empty()) {
LOG_DBG("tokenize the prompt\n");
embd_inp = common_tokenize(ctx, prompt, true, true);
} else {
LOG_DBG("use session tokens\n");
embd_inp = session_tokens;
}
LOG_DBG("prompt: \"%s\"\n", prompt.c_str());
LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str());
}
// Should not run without any tokens
if (!waiting_for_first_input && embd_inp.empty()) {
if (add_bos) {
embd_inp.push_back(llama_vocab_bos(vocab));
LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
} else {
LOG_ERR("input is empty\n");
return -1;
}
}
// Tokenize negative prompt
if ((int) embd_inp.size() > n_ctx - 4) {
LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
return 1;
}
// debug message about similarity of saved session, if applicable
size_t n_matching_session_tokens = 0;
if (!session_tokens.empty()) {
for (llama_token id : session_tokens) {
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
break;
}
n_matching_session_tokens++;
}
if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
LOG_INF("%s: using full prompt from session file\n", __func__);
} else if (n_matching_session_tokens >= embd_inp.size()) {
LOG_INF("%s: session file has exact match for prompt!\n", __func__);
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
LOG_WRN("%s: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
__func__, n_matching_session_tokens, embd_inp.size());
} else {
LOG_INF("%s: session file matches %zu / %zu tokens of prompt\n",
__func__, n_matching_session_tokens, embd_inp.size());
}
// remove any "future" tokens that we might have inherited from the previous session
llama_kv_self_seq_rm(ctx, -1, n_matching_session_tokens, -1);
}
LOG_DBG("recalculate the cached logits (check): embd_inp.size() %zu, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu\n",
embd_inp.size(), n_matching_session_tokens, embd_inp.size(), session_tokens.size());
// if we will use the cache for the full prompt without reaching the end of the cache, force
// reevaluation of the last token to recalculate the cached logits
if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
LOG_DBG("recalculate the cached logits (do): session_tokens.resize( %zu )\n", embd_inp.size() - 1);
session_tokens.resize(embd_inp.size() - 1);
}
// number of tokens to keep when resetting context
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {
params.n_keep = (int)embd_inp.size();
} else {
params.n_keep += add_bos; // always keep the BOS token
}
if (params.conversation_mode) {
if (params.single_turn && !params.prompt.empty()) {
params.interactive = false;
params.interactive_first = false;
} else {
params.interactive_first = true;
}
}
// enable interactive mode if interactive start is specified
if (params.interactive_first) {
params.interactive = true;
}
if (params.verbose_prompt) {
LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str());
}
if (params.n_keep > add_bos) {
LOG_INF("%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) {
LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str());
}
LOG_CNT("'\n");
}
LOG_INF("\n");
}
// ctrl+C handling
{
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
struct sigaction sigint_action;
sigint_action.sa_handler = sigint_handler;
sigemptyset (&sigint_action.sa_mask);
sigint_action.sa_flags = 0;
sigaction(SIGINT, &sigint_action, NULL);
#elif defined (_WIN32)
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
}
if (params.interactive) {
LOG_INF("%s: interactive mode on.\n", __func__);
if (!params.antiprompt.empty()) {
for (const auto & antiprompt : params.antiprompt) {
LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str());
if (params.verbose_prompt) {
auto tmp = common_tokenize(ctx, antiprompt, false, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
}
}
}
}
if (params.input_prefix_bos) {
LOG_INF("Input prefix with BOS\n");
}
if (!params.input_prefix.empty()) {
LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
if (params.verbose_prompt) {
auto tmp = common_tokenize(ctx, params.input_prefix, true, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
}
}
}
if (!params.input_suffix.empty()) {
LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
if (params.verbose_prompt) {
auto tmp = common_tokenize(ctx, params.input_suffix, false, true);
for (int i = 0; i < (int) tmp.size(); i++) {
LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
}
}
}
}
smpl = common_sampler_init(model, sparams);
if (!smpl) {
LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
return 1;
}
LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl));
LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str());
LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
// group-attention state
// number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
int ga_i = 0;
const int ga_n = params.grp_attn_n;
const int ga_w = params.grp_attn_w;
if (ga_n != 1) {
GGML_ASSERT(ga_n > 0 && "grp_attn_n must be positive"); // NOLINT
GGML_ASSERT(ga_w % ga_n == 0 && "grp_attn_w must be a multiple of grp_attn_n"); // NOLINT
//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of grp_attn_w"); // NOLINT
//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
LOG_INF("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
}
LOG_INF("\n");
if (params.interactive) {
const char * control_message;
if (params.multiline_input) {
control_message = " - To return control to the AI, end your input with '\\'.\n"
" - To return control without starting a new line, end your input with '/'.\n";
} else {
control_message = " - Press Return to return control to the AI.\n"
" - To return control without starting a new line, end your input with '/'.\n"
" - If you want to submit another line, end your input with '\\'.\n";
}
LOG_INF("== Running in interactive mode. ==\n");
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
LOG_INF( " - Press Ctrl+C to interject at any time.\n");
#endif
LOG_INF( "%s", control_message);
if (params.conversation_mode && params.enable_chat_template && params.system_prompt.empty()) {
LOG_INF( " - Not using system message. To change it, set a different value via -sys PROMPT\n");
}
LOG_INF("\n");
is_interacting = params.interactive_first;
}
bool is_antiprompt = false;
bool input_echo = true;
bool display = true;
bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();
int n_past = 0;
int n_remain = params.n_predict;
int n_consumed = 0;
int n_session_consumed = 0;
std::vector<int> input_tokens; g_input_tokens = &input_tokens;
std::vector<int> output_tokens; g_output_tokens = &output_tokens;
std::ostringstream output_ss; g_output_ss = &output_ss;
std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode
// the first thing we will do is to output the prompt, so set color accordingly
console::set_display(console::prompt);
display = params.display_prompt;
std::vector<llama_token> embd;
// single-token antiprompts
std::vector<llama_token> antiprompt_token;
for (const std::string & antiprompt : params.antiprompt) {
auto ids = ::common_tokenize(ctx, antiprompt, false, true);
if (ids.size() == 1) {
antiprompt_token.push_back(ids[0]);
}
}
if (llama_model_has_encoder(model)) {
int enc_input_size = embd_inp.size();
llama_token * enc_input_buf = embd_inp.data();
if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
decoder_start_token_id = llama_vocab_bos(vocab);
}
embd_inp.clear();
embd_inp.push_back(decoder_start_token_id);
}
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (!embd.empty()) {
// Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via
// --prompt or --file which uses the same value.
int max_embd_size = n_ctx - 4;
// Ensure the input doesn't exceed the context size by truncating embd if necessary.
if ((int) embd.size() > max_embd_size) {
const int skipped_tokens = (int) embd.size() - max_embd_size;
embd.resize(max_embd_size);
console::set_display(console::error);
LOG_WRN("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console::set_display(console::reset);
}
if (ga_n == 1) {
// infinite text generation via context shifting
// if we run out of context:
// - take the n_keep first tokens from the original prompt (via n_past)
// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
if (n_past + (int) embd.size() >= n_ctx) {
if (!params.ctx_shift){
LOG_DBG("\n\n%s: context full and context shift is disabled => stopping\n", __func__);
break;
}
if (params.n_predict == -2) {
LOG_DBG("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
break;
}
const int n_left = n_past - params.n_keep;
const int n_discard = n_left/2;
LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
n_past, n_left, n_ctx, params.n_keep, n_discard);
llama_kv_self_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard);
llama_kv_self_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
n_past -= n_discard;
LOG_DBG("after swap: n_past = %d\n", n_past);
LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
LOG_DBG("clear session path\n");
path_session.clear();
}
} else {
// context extension via Self-Extend
while (n_past >= ga_i + ga_w) {
const int ib = (ga_n*ga_i)/ga_w;
const int bd = (ga_w/ga_n)*(ga_n - 1);
const int dd = (ga_w/ga_n) - ib*bd - ga_w;
LOG_DBG("\n");
LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd);
LOG_DBG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
llama_kv_self_seq_add(ctx, 0, ga_i, n_past, ib*bd);
llama_kv_self_seq_div(ctx, 0, ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n);
llama_kv_self_seq_add(ctx, 0, ga_i + ib*bd + ga_w, n_past + ib*bd, dd);
n_past -= bd;
ga_i += ga_w/ga_n;
LOG_DBG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
}
}
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
if (n_session_consumed < (int) session_tokens.size()) {
size_t i = 0;
for ( ; i < embd.size(); i++) {
if (embd[i] != session_tokens[n_session_consumed]) {
session_tokens.resize(n_session_consumed);
break;
}
n_past++;
n_session_consumed++;
if (n_session_consumed >= (int) session_tokens.size()) {
++i;
break;
}
}
if (i > 0) {
embd.erase(embd.begin(), embd.begin() + i);
}
}
for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
int n_eval = (int) embd.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
n_past += n_eval;
LOG_DBG("n_past = %d\n", n_past);
// Display total tokens alongside total time
if (params.n_print > 0 && n_past % params.n_print == 0) {
LOG_DBG("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx);
}
}
if (!embd.empty() && !path_session.empty()) {
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
n_session_consumed = session_tokens.size();
}
}
embd.clear();
if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
// optionally save the session on first sample (for faster prompt loading next time)
if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
need_to_save_session = false;
llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
LOG_DBG("saved session to %s\n", path_session.c_str());
}
const llama_token id = common_sampler_sample(smpl, ctx, -1);
common_sampler_accept(smpl, id, /* accept_grammar= */ true);
// LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
embd.push_back(id);
// echo this to console
input_echo = true;
// decrement remaining sampling budget
--n_remain;
LOG_DBG("n_remain: %d\n", n_remain);
} else {
// some user input remains from prompt or interaction, forward it to processing
LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
while ((int) embd_inp.size() > n_consumed) {
embd.push_back(embd_inp[n_consumed]);
// push the prompt in the sampling context in order to apply repetition penalties later
// for the prompt, we don't apply grammar rules
common_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
++n_consumed;
if ((int) embd.size() >= params.n_batch) {
break;
}
}
}
// display text
if (input_echo && display) {
for (auto id : embd) {
const std::string token_str = common_token_to_piece(ctx, id, params.special);
// Console/Stream Output
LOG("%s", token_str.c_str());
// Record Displayed Tokens To Log
// Note: Generated tokens are created one by one hence this check
if (embd.size() > 1) {
// Incoming Requested Tokens
input_tokens.push_back(id);
} else {
// Outgoing Generated Tokens
output_tokens.push_back(id);
output_ss << token_str;
}
}
}
// reset color to default if there is no pending user input
if (input_echo && (int) embd_inp.size() == n_consumed) {
console::set_display(console::reset);
display = true;
}
// if not currently processing queued inputs;
if ((int) embd_inp.size() <= n_consumed) {
// check for reverse prompt in the last n_prev tokens
if (!params.antiprompt.empty()) {
const int n_prev = 32;
const std::string last_output = common_sampler_prev_str(smpl, ctx, n_prev);
is_antiprompt = false;
// Check if each of the reverse prompts appears at the end of the output.
// If we're not running interactively, the reverse prompt might be tokenized with some following characters
// so we'll compensate for that by widening the search window a bit.
for (std::string & antiprompt : params.antiprompt) {
size_t extra_padding = params.interactive ? 0 : 2;
size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
: 0;
if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
if (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
break;
}
}
// check for reverse prompt using special tokens
llama_token last_token = common_sampler_last(smpl);
for (auto token : antiprompt_token) {
if (token == last_token) {
if (params.interactive) {
is_interacting = true;
}
is_antiprompt = true;
break;
}
}
if (is_antiprompt) {
LOG_DBG("found antiprompt: %s\n", last_output.c_str());
}
}
// deal with end of generation tokens in interactive mode
if (!waiting_for_first_input && llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
LOG_DBG("found an EOG token\n");
if (params.interactive) {
if (!params.antiprompt.empty()) {
// tokenize and inject first reverse prompt
const auto first_antiprompt = common_tokenize(ctx, params.antiprompt.front(), false, true);
embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
is_antiprompt = true;
}
if (params.enable_chat_template) {
chat_add_and_format("assistant", assistant_ss.str());
}
is_interacting = true;
LOG("\n");
}
}
// if current token is not EOG, we add it to current assistant message
if (params.conversation_mode && !waiting_for_first_input) {
const auto id = common_sampler_last(smpl);
assistant_ss << common_token_to_piece(ctx, id, false);
if (!prompt.empty()) {
prompt.clear();
is_interacting = false;
}
}
if ((n_past > 0 || waiting_for_first_input) && is_interacting) {
LOG_DBG("waiting for user input\n");
if (params.conversation_mode) {
LOG("\n> ");
}
if (params.input_prefix_bos) {
LOG_DBG("adding input prefix BOS token\n");
embd_inp.push_back(llama_vocab_bos(vocab));
}
std::string buffer;
if (!params.input_prefix.empty() && !params.conversation_mode) {
LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str());
LOG("%s", params.input_prefix.c_str());
}
// color user input only
console::set_display(console::user_input);
display = params.display_prompt;
std::string line;
bool another_line = true;
do {
another_line = console::readline(line, params.multiline_input);
buffer += line;
} while (another_line);
// done taking input, reset color
console::set_display(console::reset);
display = true;
if (buffer.empty()) { // Ctrl+D on empty line exits
LOG("EOF by user\n");
break;
}
if (buffer.back() == '\n') {
// Implement #587:
// If the user wants the text to end in a newline,
// this should be accomplished by explicitly adding a newline by using \ followed by return,
// then returning control by pressing return again.
buffer.pop_back();
}
if (buffer.empty()) { // Enter key on empty line lets the user pass control back
LOG_DBG("empty line, passing control back\n");
} else { // Add tokens to embd only if the input buffer is non-empty
// append input suffix if any
if (!params.input_suffix.empty() && !params.conversation_mode) {
LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str());
LOG("%s", params.input_suffix.c_str());
}
LOG_DBG("buffer: '%s'\n", buffer.c_str());
const size_t original_size = embd_inp.size();
if (params.escape) {
string_process_escapes(buffer);
}
bool format_chat = params.conversation_mode && params.enable_chat_template;
std::string user_inp = format_chat
? chat_add_and_format("user", std::move(buffer))
: std::move(buffer);
// TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true);
const auto line_inp = common_tokenize(ctx, user_inp, false, format_chat);
const auto line_sfx = common_tokenize(ctx, params.input_suffix, false, true);
LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
// if user stop generation mid-way, we must add EOT to finish model's last response
if (need_insert_eot && format_chat) {
llama_token eot = llama_vocab_eot(vocab);
embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_vocab_eos(vocab) : eot);
need_insert_eot = false;
}
embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i];
output_tokens.push_back(token);
output_ss << common_token_to_piece(ctx, token);
}
// reset assistant message
assistant_ss.str("");
n_remain -= line_inp.size();
LOG_DBG("n_remain: %d\n", n_remain);
}
input_echo = false; // do not echo this again
}
if (n_past > 0 || waiting_for_first_input) {
if (is_interacting) {
common_sampler_reset(smpl);
}
is_interacting = false;
if (waiting_for_first_input && params.single_turn) {
params.interactive = false;
params.interactive_first = false;
}
waiting_for_first_input = false;
}
}
// end of generation
if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !(params.interactive)) {
LOG(" [end of text]\n");
break;
}
// In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
// We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
n_remain = params.n_predict;
is_interacting = true;
}
}
if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
LOG("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
}
LOG("\n\n");
common_perf_print(ctx, smpl);
common_sampler_free(smpl);
llama_backend_free();
ggml_threadpool_free_fn(threadpool);
ggml_threadpool_free_fn(threadpool_batch);
return 0;
}

5
tools/perplexity/CMakeLists.txt Normal file
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set(TARGET llama-perplexity)
add_executable(${TARGET} perplexity.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# Perplexity
The `perplexity` example can be used to calculate the so-called perplexity value of a language model over a given text corpus.
Perplexity measures how well the model can predict the next token with lower values being better.
Note that perplexity is **not** directly comparable between models, especially if they use different tokenizers.
Also note that finetunes typically result in a higher perplexity value even though the human-rated quality of outputs increases.
Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16.
The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`).
When numbers are listed all command line arguments and compilation options are left at their defaults unless noted otherwise.
llama.cpp numbers are **not** directly comparable to those of other projects because the exact values depend strongly on the implementation details.
By default only the mean perplexity value and the corresponding uncertainty is calculated.
The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation.
More statistics can be obtained by recording the logits from the FP16 version of a model.
To do this, supply `perplexity` with `--kl-divergence-base path/to/logit/binary/file.kld`.
The program will then record all logits and save them to the provided path in binary format.
**The logit file will be very large, 11 GiB for LLaMA 2 or 37 GiB for LLaMA 3 when using the Wikitext-2 test set.**
Once you have the file, supply `perplexity` with the quantized model, the logits file via `--kl-divergence-base`,
and finally the `--kl-divergence` argument to indicate that the program should calculate the so-called Kullback-Leibler divergence.
This is a measure of how similar the FP16 and the quantized logit distributions are with a value of 0 indicating that the distribution are the same.
The uncertainty on the mean KL divergence is calculated by assuming the KL divergence per token follows a Gaussian distribution.
In addition to the KL divergence the following statistics are calculated with `--kl-divergence`:
* Ratio of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same.
* Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated.
* Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse.
* Pearson correlation coefficient of the "correct" token probabilites between models.
* Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization.
* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 .
* Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution.
## LLaMA 3 8b Scoreboard
| Revision | f364eb6f |
|:---------|:-------------------|
| Backend | CUDA |
| CPU | AMD Epyc 7742 |
| GPU | 1x NVIDIA RTX 4090 |
Results were generated using the CUDA backend and are sorted by Kullback-Leibler divergence relative to FP16.
The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat).
Note: the FP16 logits used for the calculation of all metrics other than perplexity are stored in a binary file between runs.
In order to save space this file does **not** contain the exact same FP32 logits but instead casts them to 16 bit unsigned integers (with some scaling).
So the "f16" results are to be understood as the difference resulting only from this downcast.
| Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp |
|--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------|
| f16 | None | 14.97 | 6.233160 ± 0.037828 | 0.001524 ± 0.000755 | 0.000551 ± 0.000002 | 0.001 ± 0.002 % | 0.787 ± 0.004 % |
| q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % |
| q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % |
| q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % |
| q5_K_S | None | 5.21 | 6.336598 ± 0.038755 | 0.104964 ± 0.003331 | 0.016595 ± 0.000122 | -0.223 ± 0.010 % | 3.918 ± 0.036 % |
| q5_1 | None | 5.65 | 6.337857 ± 0.038677 | 0.106223 ± 0.003476 | 0.018045 ± 0.000139 | -0.287 ± 0.011 % | 4.123 ± 0.039 % |
| q5_0 | None | 5.21 | 6.363224 ± 0.038861 | 0.131591 ± 0.003894 | 0.022239 ± 0.000166 | -0.416 ± 0.012 % | 4.634 ± 0.043 % |
| q4_K_M | WT 10m | 4.58 | 6.382937 ± 0.039055 | 0.151303 ± 0.004429 | 0.028152 ± 0.000240 | -0.389 ± 0.014 % | 5.251 ± 0.049 % |
| q4_K_M | None | 4.58 | 6.407115 ± 0.039119 | 0.175482 ± 0.004620 | 0.031273 ± 0.000238 | -0.596 ± 0.014 % | 5.519 ± 0.050 % |
| q4_K_S | WT 10m | 4.37 | 6.409697 ± 0.039189 | 0.178064 ± 0.004744 | 0.031951 ± 0.000259 | -0.531 ± 0.015 % | 5.645 ± 0.051 % |
| iq4_NL | WT 10m | 4.35 | 6.455593 ± 0.039630 | 0.223959 ± 0.005201 | 0.035742 ± 0.000288 | -0.590 ± 0.016 % | 5.998 ± 0.054 % |
| iq4_XS | WT 10m | 4.14 | 6.459705 ± 0.039595 | 0.228071 ± 0.005207 | 0.036334 ± 0.000284 | -0.668 ± 0.016 % | 6.044 ± 0.054 % |
| q4_K_S | None | 4.37 | 6.500529 ± 0.039778 | 0.268895 ± 0.005638 | 0.043136 ± 0.000314 | -0.927 ± 0.017 % | 6.562 ± 0.055 % |
| q4_1 | None | 4.78 | 6.682737 ± 0.041285 | 0.451103 ± 0.008030 | 0.071683 ± 0.000505 | -0.927 ± 0.017 % | 8.512 ± 0.063 % |
| q4_0 | None | 4.34 | 6.700147 ± 0.041226 | 0.468514 ± 0.007951 | 0.071940 ± 0.000491 | -1.588 ± 0.022 % | 8.434 ± 0.061 % |
| q3_K_L | WT 10m | 4.03 | 6.671223 ± 0.041427 | 0.439590 ± 0.008154 | 0.073077 ± 0.000529 | -0.940 ± 0.023 % | 8.662 ± 0.064 % |
| q3_K_M | WT 10m | 3.74 | 6.734255 ± 0.041838 | 0.502622 ± 0.008901 | 0.084358 ± 0.000588 | -1.198 ± 0.024 % | 9.292 ± 0.065 % |
| q3_K_L | None | 4.03 | 6.787876 ± 0.042104 | 0.556242 ± 0.009171 | 0.087176 ± 0.000614 | -1.532 ± 0.025 % | 9.432 ± 0.067 % |
| q3_K_M | None | 3.74 | 6.888498 ± 0.042669 | 0.656864 ± 0.010071 | 0.101913 ± 0.000677 | -1.990 ± 0.026 % | 10.203 ± 0.068 % |
| iq3_M | WT 10m | 3.53 | 6.898327 ± 0.041643 | 0.666694 ± 0.009449 | 0.102534 ± 0.000663 | -3.178 ± 0.026 % | 10.513 ± 0.066 % |
| iq3_S | WT 10m | 3.42 | 6.965501 ± 0.042406 | 0.733867 ± 0.010245 | 0.111278 ± 0.000710 | -3.066 ± 0.027 % | 10.845 ± 0.068 % |
| iq3_XS | WT 10m | 3.28 | 7.163043 ± 0.043772 | 0.931409 ± 0.012084 | 0.138693 ± 0.000857 | -3.667 ± 0.031 % | 12.148 ± 0.070 % |
| iq3_XXS | WT 10m | 3.05 | 7.458436 ± 0.046404 | 1.226803 ± 0.015234 | 0.183625 ± 0.001042 | -3.918 ± 0.035 % | 13.836 ± 0.074 % |
| q3_K_S | WT 10m | 3.41 | 7.602878 ± 0.046848 | 1.371244 ± 0.015688 | 0.199821 ± 0.001008 | -5.046 ± 0.037 % | 14.980 ± 0.070 % |
| q3_K_S | None | 3.41 | 7.863786 ± 0.048885 | 1.632152 ± 0.017733 | 0.228217 ± 0.001079 | -5.604 ± 0.038 % | 15.541 ± 0.070 % |
| iq2_M | WT 10m | 2.74 | 8.600799 ± 0.055124 | 2.369166 ± 0.025244 | 0.325989 ± 0.00160 | -6.463 ± 0.046 % | 18.519 ± 0.080 % |
| q2_K | WT 10k | 2.96 | 8.652290 ± 0.055572 | 2.420657 ± 0.025587 | 0.331393 ± 0.001562 | -6.606 ± 0.046 % | 18.790 ± 0.078 % |
| q2_K | WT 100k | 2.96 | 8.641993 ± 0.055406 | 2.410359 ± 0.025495 | 0.331672 ± 0.001569 | -6.628 ± 0.047 % | 18.856 ± 0.078 % |
| q2_K | WT 10m | 2.96 | 8.647825 ± 0.055610 | 2.416191 ± 0.025683 | 0.332223 ± 0.001572 | -6.500 ± 0.047 % | 18.881 ± 0.078 % |
| q2_K | WT 1m | 2.96 | 8.674365 ± 0.055743 | 2.442732 ± 0.025843 | 0.335308 ± 0.001576 | -6.634 ± 0.047 % | 19.009 ± 0.079 % |
| q2_K | WT 1k | 2.96 | 8.682605 ± 0.055916 | 2.450972 ± 0.026069 | 0.337093 ± 0.001596 | -6.596 ± 0.047 % | 18.977 ± 0.079 % |
| q2_K_S | WT 10m | 2.96 | 9.323778 ± 0.061551 | 3.092145 ± 0.031914 | 0.403360 ± 0.001787 | -7.131 ± 0.049 % | 20.050 ± 0.081 % |
| q2_K_S | WT 1m | 2.96 | 9.329321 ± 0.061378 | 3.097688 ± 0.031816 | 0.403590 ± 0.001797 | -7.289 ± 0.049 % | 20.123 ± 0.081 % |
| q2_K_S | WT 100k | 2.96 | 9.362973 ± 0.061740 | 3.131339 ± 0.032169 | 0.408367 ± 0.001802 | -7.198 ± 0.050 % | 20.132 ± 0.081 % |
| q2_K_S | WT 10k | 2.96 | 9.376479 ± 0.062045 | 3.144846 ± 0.032464 | 0.408662 ± 0.001819 | -7.141 ± 0.050 % | 20.120 ± 0.081 % |
| q2_K_S | WT 1k | 2.96 | 9.415200 ± 0.062475 | 3.183567 ± 0.032993 | 0.415865 ± 0.001846 | -7.153 ± 0.050 % | 20.311 ± 0.082 % |
| iq2_S | WT 10m | 2.56 | 9.650781 ± 0.063209 | 3.419148 ± 0.034017 | 0.439197 ± 0.001976 | -8.319 ± 0.052 % | 21.491 ± 0.083 % |
| q2_K | None | 2.96 | 9.751568 ± 0.063312 | 3.519934 ± 0.033863 | 0.445132 ± 0.001835 | -9.123 ± 0.051 % | 21.421 ± 0.079 % |
| iq2_XS | WT 10m | 2.43 | 10.761424 ± 0.071056 | 4.529791 ± 0.042229 | 0.546290 ± 0.002133 | -10.576 ± 0.056 % | 23.872 ± 0.082 % |
| iq2_XXS | WT 10m | 2.24 | 14.091782 ± 0.098396 | 7.860148 ± 0.070752 | 0.812022 ± 0.002741 | -14.363 ± 0.065 % | 28.576 ± 0.084 % |
| iq1_M | WT 10m | 2.01 | 25.493722 ± 0.177903 | 19.262089 ± 0.152396 | 1.393084 ± 0.003529 | -24.672 ± 0.077 % | 38.287 ± 0.084 % |
| iq1_S | WT 1m | 1.88 | 58.097760 ± 0.438604 | 51.866126 ± 0.416604 | 2.211278 ± 0.004688 | -32.471 ± 0.087 % | 46.418 ± 0.085 % |
| iq1_S | WT 1k | 1.88 | 58.267851 ± 0.446208 | 52.036218 ± 0.424373 | 2.214858 ± 0.004778 | -31.880 ± 0.089 % | 46.330 ± 0.086 % |
| iq1_S | WT 100k | 1.88 | 58.581498 ± 0.453145 | 52.349864 ± 0.431360 | 2.220834 ± 0.004818 | -32.261 ± 0.089 % | 46.002 ± 0.086 % |
| iq1_S | WT 10m | 1.88 | 60.694593 ± 0.471290 | 54.462959 ± 0.449644 | 2.254554 ± 0.004868 | -31.973 ± 0.088 % | 46.271 ± 0.086 % |
| iq1_S | WT 10k | 1.88 | 63.221324 ± 0.493077 | 56.989691 ± 0.471423 | 2.293527 ± 0.004885 | -32.261 ± 0.089 % | 46.562 ± 0.086 % |
There seems to be no consistent improvement from using more Wikitext tokens for the importance matrix.
K-quants score better on mean Δp than the legacy quants than e.g. KL divergence would suggest.
## LLaMA 2 vs. LLaMA 3 Quantization comparison
| Revision | f364eb6f |
|:---------|:-------------------|
| Backend | CUDA |
| CPU | AMD Epyc 7742 |
| GPU | 1x NVIDIA RTX 4090 |
| Metric | L2 7b q2_K | L3 8b q2_K | L2 7b q4_K_M | L3 8b q4_K_M | L2 7b q6_K | L3 8b q6_K | L2 7b q8_0 | L3 8b q8_0 |
|-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|
| Mean PPL | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 |
| Mean PPL ratio | 1.107955 ± 0.001427 | 1.564849 ± 0.004525 | 1.014242 ± 0.000432 | 1.028160 ± 0.000723 | 1.002406 ± 0.000191 | 1.003490 ± 0.000296 | 1.000689 ± 0.000107 | 1.000425 ± 0.000161 |
| Mean ΔPPL | 0.625552 ± 0.008725 | 3.519934 ± 0.033863 | 0.082526 ± 0.002530 | 0.175482 ± 0.004620 | 0.013941 ± 0.001110 | 0.021748 ± 0.001852 | 0.003990 ± 0.000624 | 0.002650 ± 0.001006 |
| PPL correlation | 97.36% | 89.62% | 99.71% | 99.34% | 99.94% | 99.88% | 99.98% | 99.96% |
| Mean KLD | 0.108903 ± 0.000645 | 0.445132 ± 0.001835 | 0.012686 ± 0.000079 | 0.031273 ± 0.000238 | 0.002098 ± 0.000014 | 0.005452 ± 0.000035 | 0.000369 ± 0.000007 | 0.001355 ± 0.000006 |
| Mean Δp | -2.710 ± 0.023 % | -9.123 ± 0.051 % | -0.416 ± 0.008 % | -0.596 ± 0.014 % | -0.035 ± 0.003 % | -0.007 ± 0.006 % | -0.005 ± 0.002 % | -0.019 ± 0.003 % |
| Maximum Δp | 85.136% | 94.268% | 45.209% | 95.054% | 23.593% | 53.601% | 43.925% | 28.734% |
| 99.9% Δp | 37.184% | 50.003% | 17.461% | 27.084% | 7.798% | 13.613% | 3.387% | 6.402% |
| 99.0% Δp | 18.131% | 25.875% | 7.798% | 12.084% | 3.838% | 6.407% | 1.867% | 3.544% |
| Median Δp | -0.391% | -2.476% | -0.026% | -0.024% | -0.001% | 0.000% | -0.000% | -0.000% |
| 1.0% Δp | -39.762% | -87.173% | -11.433% | -19.567% | -4.222% | -6.767% | -1.862% | -3.698% |
| 0.1% Δp | -79.002% | -98.897% | -26.433% | -56.054% | -9.091% | -16.584% | -3.252% | -6.579% |
| Minimum Δp | -99.915% | -99.965% | -83.383% | -98.699% | -43.142% | -68.487% | -9.343% | -24.301% |
| RMS Δp | 9.762 ± 0.053 % | 21.421 ± 0.079 % | 3.252 ± 0.024 % | 5.519 ± 0.050 % | 1.339 ± 0.010 % | 2.295 ± 0.019 % | 0.618 ± 0.011 % | 1.198 ± 0.007 % |
| Same top p | 85.584 ± 0.086 % | 71.138 ± 0.119 % | 94.665 ± 0.055 % | 91.901 ± 0.072 % | 97.520 ± 0.038 % | 96.031 ± 0.051 % | 98.846 ± 0.026 % | 97.674 ± 0.040 % |
## LLaMA 3 BF16 vs. FP16 comparison
| Revision | 83330d8c |
|:---------|:--------------|
| Backend | CPU |
| CPU | AMD Epyc 7742 |
| GPU | N/A |
Results were calculated with LLaMA 3 8b BF16 as `--kl-divergence-base` and LLaMA 3 8b FP16 as the `--model` for comparison.
| Metric | Value |
|--------------------------------|--------------------------|
| Mean PPL(Q) | 6.227711 ± 0.037833 |
| Mean PPL(base) | 6.225194 ± 0.037771 |
| Cor(ln(PPL(Q)), ln(PPL(base))) | 99.990% |
| Mean ln(PPL(Q)/PPL(base)) | 0.000404 ± 0.000086 |
| Mean PPL(Q)/PPL(base) | 1.000404 ± 0.000086 |
| Mean PPL(Q)-PPL(base) | 0.002517 ± 0.000536 |
| Mean KLD | 0.00002515 ± 0.00000020 |
| Maximum KLD | 0.012206 |
| 99.9% KLD | 0.000799 |
| 99.0% KLD | 0.000222 |
| 99.0% KLD | 0.000222 |
| Median KLD | 0.000013 |
| 10.0% KLD | -0.000002 |
| 5.0% KLD | -0.000008 |
| 1.0% KLD | -0.000023 |
| Minimum KLD | -0.000059 |
| Mean Δp | -0.0000745 ± 0.0003952 % |
| Maximum Δp | 4.186% |
| 99.9% Δp | 1.049% |
| 99.0% Δp | 0.439% |
| 95.0% Δp | 0.207% |
| 90.0% Δp | 0.125% |
| 75.0% Δp | 0.029% |
| Median Δp | 0.000% |
| 25.0% Δp | -0.030% |
| 10.0% Δp | -0.126% |
| 5.0% Δp | -0.207% |
| 1.0% Δp | -0.434% |
| 0.1% Δp | -1.016% |
| Minimum Δp | -4.672% |
| RMS Δp | 0.150 ± 0.001 % |
| Same top p | 99.739 ± 0.013 % |
## Old Numbers
<details>
<summary>Llama 2 70B Scoreboard</summary>
| Quantization | Model size (GiB) | Perplexity | Delta to fp16 |
|--------------|------------------|------------|---------------|
| Q4_0 | 36.20 | 3.5550 | 3.61% |
| Q4_1 | 40.20 | 3.5125 | 2.37% |
| Q5_0 | 44.20 | 3.4744 | 1.26% |
| Q2_K | 27.27 | 3.7339 | 8.82% |
| Q3_K_S | 27.86 | 3.7019 | 7.89% |
| Q3_K_M | 30.83 | 3.5932 | 4.72% |
| Q3_K_L | 33.67 | 3.5617 | 3.80% |
| Q4_K_S | 36.39 | 3.4852 | 1.57% |
| Q4_K_M | 38.54 | 3.4725 | 1.20% |
| Q5_K_S | 44.20 | 3.4483 | 0.50% |
| Q5_K_M | 45.41 | 3.4451 | 0.40% |
| Q6_K | 52.70 | 3.4367 | 0.16% |
| fp16 | 128.5 | 3.4313 | - |
</details>

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set(TARGET llama-quantize)
add_executable(${TARGET} quantize.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_include_directories(${TARGET} PRIVATE ../../common)
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# quantize
You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to build your own quants without any setup.
Note: It is synced from llama.cpp `main` every 6 hours.
Example usage:
```bash
# obtain the official LLaMA model weights and place them in ./models
ls ./models
llama-2-7b tokenizer_checklist.chk tokenizer.model
# [Optional] for models using BPE tokenizers
ls ./models
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>
# install Python dependencies
python3 -m pip install -r requirements.txt
# convert the model to ggml FP16 format
python3 convert_hf_to_gguf.py models/mymodel/
# quantize the model to 4-bits (using Q4_K_M method)
./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
# update the gguf filetype to current version if older version is now unsupported
./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
```
Run the quantized model:
```bash
# start inference on a gguf model
./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -cnv -p "You are a helpful assistant"
```
When running the larger models, make sure you have enough disk space to store all the intermediate files.
## Memory/Disk Requirements
As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
| Model | Original size | Quantized size (Q4_0) |
|------:|--------------:|----------------------:|
| 7B | 13 GB | 3.9 GB |
| 13B | 24 GB | 7.8 GB |
| 30B | 60 GB | 19.5 GB |
| 65B | 120 GB | 38.5 GB |
## Quantization
Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
*(outdated)*
| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 |
|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |
| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 |
| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 |
| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G |
| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 |
| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 |
| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
- [k-quants](https://github.com/ggml-org/llama.cpp/pull/1684)
- recent k-quants improvements and new i-quants
- [#2707](https://github.com/ggml-org/llama.cpp/pull/2707)
- [#2807](https://github.com/ggml-org/llama.cpp/pull/2807)
- [#4773 - 2-bit i-quants (inference)](https://github.com/ggml-org/llama.cpp/pull/4773)
- [#4856 - 2-bit i-quants (inference)](https://github.com/ggml-org/llama.cpp/pull/4856)
- [#4861 - importance matrix](https://github.com/ggml-org/llama.cpp/pull/4861)
- [#4872 - MoE models](https://github.com/ggml-org/llama.cpp/pull/4872)
- [#4897 - 2-bit quantization](https://github.com/ggml-org/llama.cpp/pull/4897)
- [#4930 - imatrix for all k-quants](https://github.com/ggml-org/llama.cpp/pull/4930)
- [#4951 - imatrix on the GPU](https://github.com/ggml-org/llama.cpp/pull/4957)
- [#4969 - imatrix for legacy quants](https://github.com/ggml-org/llama.cpp/pull/4969)
- [#4996 - k-quants tuning](https://github.com/ggml-org/llama.cpp/pull/4996)
- [#5060 - Q3_K_XS](https://github.com/ggml-org/llama.cpp/pull/5060)
- [#5196 - 3-bit i-quants](https://github.com/ggml-org/llama.cpp/pull/5196)
- [quantization tuning](https://github.com/ggml-org/llama.cpp/pull/5320), [another one](https://github.com/ggml-org/llama.cpp/pull/5334), and [another one](https://github.com/ggml-org/llama.cpp/pull/5361)
**Llama 2 7B**
| Quantization | Bits per Weight (BPW) |
|--------------|-----------------------|
| Q2_K | 3.35 |
| Q3_K_S | 3.50 |
| Q3_K_M | 3.91 |
| Q3_K_L | 4.27 |
| Q4_K_S | 4.58 |
| Q4_K_M | 4.84 |
| Q5_K_S | 5.52 |
| Q5_K_M | 5.68 |
| Q6_K | 6.56 |
**Llama 2 13B**
Quantization | Bits per Weight (BPW)
-- | --
Q2_K | 3.34
Q3_K_S | 3.48
Q3_K_M | 3.89
Q3_K_L | 4.26
Q4_K_S | 4.56
Q4_K_M | 4.83
Q5_K_S | 5.51
Q5_K_M | 5.67
Q6_K | 6.56
**Llama 2 70B**
Quantization | Bits per Weight (BPW)
-- | --
Q2_K | 3.40
Q3_K_S | 3.47
Q3_K_M | 3.85
Q3_K_L | 4.19
Q4_K_S | 4.53
Q4_K_M | 4.80
Q5_K_S | 5.50
Q5_K_M | 5.65
Q6_K | 6.56

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#include "common.h"
#include "llama.h"
#include <cstdio>
#include <cstring>
#include <vector>
#include <string>
#include <unordered_map>
#include <fstream>
#include <cmath>
#include <cctype>
#include <algorithm>
struct quant_option {
std::string name;
llama_ftype ftype;
std::string desc;
};
static const std::vector<quant_option> QUANT_OPTIONS = {
{ "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 4.78G, +0.4511 ppl @ Llama-3-8B", },
{ "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 5.21G, +0.1316 ppl @ Llama-3-8B", },
{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 5.65G, +0.1062 ppl @ Llama-3-8B", },
{ "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS, " 2.06 bpw quantization", },
{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", },
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "TQ1_0", LLAMA_FTYPE_MOSTLY_TQ1_0, " 1.69 bpw ternarization", },
{ "TQ2_0", LLAMA_FTYPE_MOSTLY_TQ2_0, " 2.06 bpw ternarization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.96G, +3.5199 ppl @ Llama-3-8B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.96G, +3.1836 ppl @ Llama-3-8B", },
{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", },
{ "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", },
{ "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", },
{ "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
{ "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization", },
{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 3.41G, +1.6321 ppl @ Llama-3-8B", },
{ "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.74G, +0.6569 ppl @ Llama-3-8B", },
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 4.03G, +0.5562 ppl @ Llama-3-8B", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
{ "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 4.37G, +0.2689 ppl @ Llama-3-8B", },
{ "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 4.58G, +0.1754 ppl @ Llama-3-8B", },
{ "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
{ "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 5.21G, +0.1049 ppl @ Llama-3-8B", },
{ "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B", },
{ "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 6.14G, +0.0217 ppl @ Llama-3-8B", },
{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 7.96G, +0.0026 ppl @ Llama-3-8B", },
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", },
{ "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", },
{ "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", },
// Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
{ "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", },
};
static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file";
static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset";
static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count";
static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count";
static bool striequals(const char * a, const char * b) {
while (*a && *b) {
if (std::tolower(*a) != std::tolower(*b)) {
return false;
}
a++; b++;
}
return *a == *b;
}
static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
std::string ftype_str;
for (auto ch : ftype_str_in) {
ftype_str.push_back(std::toupper(ch));
}
for (auto & it : QUANT_OPTIONS) {
if (striequals(it.name.c_str(), ftype_str.c_str())) {
ftype = it.ftype;
ftype_str_out = it.name;
return true;
}
}
try {
int ftype_int = std::stoi(ftype_str);
for (auto & it : QUANT_OPTIONS) {
if (it.ftype == ftype_int) {
ftype = it.ftype;
ftype_str_out = it.name;
return true;
}
}
}
catch (...) {
// stoi failed
}
return false;
}
// usage:
// ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type]\n", executable);
printf(" [--token-embedding-type] [--tensor-type] [--keep-split] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n");
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --tensor-type TENSOR=TYPE: quantize this tensor to this ggml_type. example: --tensor-type attn_q=q8_0\n");
printf(" Advanced option to selectively quantize tensors. May be specified multiple times.\n");
printf(" --keep-split: will generate quantized model in the same shards as input\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
printf("Note: --include-weights and --exclude-weights cannot be used together\n");
printf("\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
if (it.name != "COPY") {
printf(" %2d or ", it.ftype);
} else {
printf(" ");
}
printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str());
}
exit(1);
}
static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
exit(1);
}
int n_entries;
in.read((char *)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
printf("%s: no data in file %s\n", __func__, imatrix_file.c_str());
exit(1);
}
for (int i = 0; i < n_entries; ++i) {
int len; in.read((char *)&len, sizeof(len));
std::vector<char> name_as_vec(len+1);
in.read((char *)name_as_vec.data(), len);
if (in.fail()) {
printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str());
exit(1);
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
auto & e = imatrix_data[name];
int ncall;
in.read((char *)&ncall, sizeof(ncall));
int nval;
in.read((char *)&nval, sizeof(nval));
if (in.fail() || nval < 1) {
printf("%s: failed reading number of values for entry %d\n", __func__, i);
imatrix_data = {};
exit(1);
}
e.resize(nval);
in.read((char *)e.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n", __func__, i);
imatrix_data = {};
exit(1);
}
if (ncall > 0) {
for (auto& v : e) v /= ncall;
}
if (getenv("LLAMA_TRACE")) {
printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
}
}
// latest imatrix version contains the dataset filename at the end of the file
int m_last_call = 0;
if (in.peek() != EOF) {
in.read((char *)&m_last_call, sizeof(m_last_call));
int dataset_len;
in.read((char *)&dataset_len, sizeof(dataset_len));
std::vector<char> dataset_as_vec(dataset_len);
in.read(dataset_as_vec.data(), dataset_len);
imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
}
printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
return m_last_call;
}
static int prepare_imatrix(const std::string & imatrix_file,
std::string & imatrix_dataset,
const std::vector<std::string> & included_weights,
const std::vector<std::string> & excluded_weights,
std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
int m_last_call = -1;
if (!imatrix_file.empty()) {
m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
}
if (imatrix_data.empty()) {
return m_last_call;
}
if (!excluded_weights.empty()) {
for (auto& name : excluded_weights) {
for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) {
auto pos = it->first.find(name);
if (pos != std::string::npos) it = imatrix_data.erase(it);
else ++it;
}
}
}
if (!included_weights.empty()) {
std::unordered_map<std::string, std::vector<float>> tmp;
for (auto& name : included_weights) {
for (auto& e : imatrix_data) {
auto pos = e.first.find(name);
if (pos != std::string::npos) {
tmp.emplace(std::move(e));
}
}
}
imatrix_data = std::move(tmp);
}
if (!imatrix_data.empty()) {
printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
}
return m_last_call;
}
static ggml_type parse_ggml_type(const char * arg) {
for (int i = 0; i < GGML_TYPE_COUNT; ++i) {
auto type = (ggml_type)i;
const auto * name = ggml_type_name(type);
if (name && striequals(name, arg)) {
return type;
}
}
fprintf(stderr, "%s: invalid ggml_type '%s'\n", __func__, arg);
return GGML_TYPE_COUNT;
}
// Allowed tensors for arbitrary quantization with --tensor-type option
static const std::vector<std::string> ALLOWED_TENSOR_TYPE = {
"attn_k",
"attn_kv_a_mqa",
"attn_kv_b",
"attn_o",
"attn_output",
"attn_q",
"attn_q_a",
"attn_q_b",
"attn_qkv",
"attn_v",
"channel_mix_key",
"channel_mix_receptance",
"channel_mix_value",
"cls",
"cls.output",
"cross_attn_k",
"cross_attn_o",
"cross_attn_q",
"cross_attn_v",
"ffn_act",
"ffn_down",
"ffn_down_exps",
"ffn_down_shexp",
"ffn_gate",
"ffn_gate_exps",
"ffn_gate_shexp",
"ffn_up",
"ffn_up_exps",
"ffn_up_shexp",
"ssm_in",
"ssm_out",
"time_mix_gate",
"time_mix_key",
"time_mix_output",
"time_mix_receptance",
"time_mix_value",
};
// changes to this struct must be replicated in llama-quant.cpp
struct tensor_quantization {
std::string name;
ggml_type quant = GGML_TYPE_COUNT;
};
static bool parse_tensor_type(const char * data, std::vector<tensor_quantization> & tensor_type) {
const char * sep = strchr(data, '=');
if (sep == nullptr) {
printf("\n%s: malformed tensor type '%s'\n\n", __func__, data);
return false;
}
const size_t tn_len = sep - data;
if (tn_len == 0) {
printf("\n%s: missing tensor name\n\n", __func__);
return false;
}
if (const size_t qt_len = strlen(sep); qt_len == 1) {
printf("\n%s: missing quantization type\n\n", __func__);
return false;
}
std::string tn(data, tn_len);
std::transform(tn.begin(), tn.end(), tn.begin(), tolower);
sep++;
const std::string qt(sep);
bool found = false;
for (const auto & allowed : ALLOWED_TENSOR_TYPE) {
std::string tensor;
tensor = tn.rfind('.') != std::string::npos ? tn.substr(tn.rfind('.') + 1) : tn;
// handle special case of cls.output
std::string cls_output = "cls.output";
if (tn.find(cls_output) != std::string::npos) {
tensor = "cls.output";
}
// check if an allowed tensor exists and it's at the end of the kv string
if (tensor == allowed) {
found = true;
break;
}
}
if (!found) {
printf("\n%s: invalid tensor name '%s'\n\n", __func__, tn.c_str());
return false;
}
if (parse_ggml_type(qt.c_str()) == GGML_TYPE_COUNT) {
printf("\n%s: invalid quantization type '%s'\n\n", __func__, qt.c_str());
return false;
}
tensor_quantization tqz;
tqz.name = tn;
tqz.quant = parse_ggml_type(qt.c_str());
tensor_type.emplace_back(std::move(tqz));
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
}
llama_model_quantize_params params = llama_model_quantize_default_params();
int arg_idx = 1;
std::string imatrix_file;
std::vector<std::string> included_weights, excluded_weights;
std::vector<llama_model_kv_override> kv_overrides;
std::vector<tensor_quantization> tensor_types;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
params.quantize_output_tensor = false;
} else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) {
if (arg_idx < argc-1) {
params.output_tensor_type = parse_ggml_type(argv[++arg_idx]);
if (params.output_tensor_type == GGML_TYPE_COUNT) {
usage(argv[0]);
}
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) {
if (arg_idx < argc-1) {
params.token_embedding_type = parse_ggml_type(argv[++arg_idx]);
if (params.token_embedding_type == GGML_TYPE_COUNT) {
usage(argv[0]);
}
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--tensor-type") == 0) {
if (arg_idx == argc-1 || !parse_tensor_type(argv[++arg_idx], tensor_types)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
params.allow_requantize = true;
} else if (strcmp(argv[arg_idx], "--pure") == 0) {
params.pure = true;
} else if (strcmp(argv[arg_idx], "--imatrix") == 0) {
if (arg_idx < argc-1) {
imatrix_file = argv[++arg_idx];
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--include-weights") == 0) {
if (arg_idx < argc-1) {
included_weights.emplace_back(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) {
if (arg_idx < argc-1) {
excluded_weights.emplace_back(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--keep-split") == 0) {
params.keep_split = true;
} else {
usage(argv[0]);
}
}
if (argc - arg_idx < 2) {
printf("%s: bad arguments\n", argv[0]);
usage(argv[0]);
}
if (!included_weights.empty() && !excluded_weights.empty()) {
usage(argv[0]);
}
std::string imatrix_dataset;
std::unordered_map<std::string, std::vector<float>> imatrix_data;
int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
if (!imatrix_data.empty()) {
params.imatrix = &imatrix_data;
{
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_file.c_str(), 127);
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
if (!imatrix_dataset.empty()) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
{
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = imatrix_data.size();
kv_overrides.emplace_back(std::move(kvo));
}
if (m_last_call > 0) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = m_last_call;
kv_overrides.emplace_back(std::move(kvo));
}
}
if (!kv_overrides.empty()) {
kv_overrides.emplace_back();
kv_overrides.back().key[0] = 0;
params.kv_overrides = &kv_overrides;
}
if (!tensor_types.empty()) {
params.tensor_types = &tensor_types;
}
llama_backend_init();
// parse command line arguments
const std::string fname_inp = argv[arg_idx];
arg_idx++;
std::string fname_out;
std::string ftype_str;
std::string suffix = ".gguf";
if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
std::string fpath;
const size_t pos = fname_inp.find_last_of("/\\");
if (pos != std::string::npos) {
fpath = fname_inp.substr(0, pos + 1);
}
// export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting
fname_out = fpath + "ggml-model-" + ftype_str;
if (!params.keep_split) {
fname_out += suffix;
}
arg_idx++;
if (ftype_str == "COPY") {
params.only_copy = true;
}
} else {
fname_out = argv[arg_idx];
if (params.keep_split && fname_out.find(suffix) != std::string::npos) {
fname_out = fname_out.substr(0, fname_out.length() - suffix.length());
}
arg_idx++;
if (argc <= arg_idx) {
fprintf(stderr, "%s: missing ftype\n", __func__);
return 1;
}
if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
return 1;
}
if (ftype_str == "COPY") {
params.only_copy = true;
}
arg_idx++;
}
// parse nthreads
if (argc > arg_idx) {
try {
params.nthread = std::stoi(argv[arg_idx]);
}
catch (const std::exception & e) {
fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what());
return 1;
}
}
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
fprintf(stderr, "\n==========================================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "==========================================================================================================\n\n\n");
return 1;
}
print_build_info();
fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
if (params.nthread > 0) {
fprintf(stderr, " using %d threads", params.nthread);
}
fprintf(stderr, "\n");
const int64_t t_main_start_us = llama_time_us();
int64_t t_quantize_us = 0;
// load the model
{
const int64_t t_start_us = llama_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), &params)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}
t_quantize_us = llama_time_us() - t_start_us;
}
// report timing
{
const int64_t t_main_end_us = llama_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
}
llama_backend_free();
return 0;
}

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#!/bin/bash
set -eu
if [ $# -lt 1 ]
then
echo "usage: $0 path_to_build_binary [path_to_temp_folder]"
echo "example: $0 ../../build/bin ../../tmp"
exit 1
fi
if [ $# -gt 1 ]
then
TMP_DIR=$2
else
TMP_DIR=/tmp
fi
set -x
SPLIT=$1/llama-gguf-split
QUANTIZE=$1/llama-quantize
MAIN=$1/llama-cli
WORK_PATH=$TMP_DIR/quantize
ROOT_DIR=$(realpath $(dirname $0)/../../)
mkdir -p "$WORK_PATH"
# Clean up in case of previously failed test
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf
# 1. Get a model
(
cd $WORK_PATH
"$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf
)
echo PASS
# 2. Split model
$SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split
echo PASS
echo
# 3. Requant model with '--keep-split'
$QUANTIZE --allow-requantize --keep-split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K
echo PASS
echo
# 3a. Test the requanted model is loading properly
$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32
echo PASS
echo
# 4. Requant mode without '--keep-split'
$QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K
echo PASS
echo
# 4b. Test the requanted model is loading properly
$MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32
echo PASS
echo
# Clean up
rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf

4
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set(TARGET rpc-server)
add_executable(${TARGET} rpc-server.cpp)
target_link_libraries(${TARGET} PRIVATE ggml)
target_compile_features(${TARGET} PRIVATE cxx_std_17)

85
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## Overview
> [!IMPORTANT]
> This example and the RPC backend are currently in a proof-of-concept development stage. As such, the functionality is fragile and
> insecure. **Never run the RPC server on an open network or in a sensitive environment!**
The `rpc-server` allows running `ggml` backend on a remote host.
The RPC backend communicates with one or several instances of `rpc-server` and offloads computations to them.
This can be used for distributed LLM inference with `llama.cpp` in the following way:
```mermaid
flowchart TD
rpcb<-->|TCP|srva
rpcb<-->|TCP|srvb
rpcb<-.->|TCP|srvn
subgraph hostn[Host N]
srvn[rpc-server]<-.->backend3["Backend (CUDA,Metal,etc.)"]
end
subgraph hostb[Host B]
srvb[rpc-server]<-->backend2["Backend (CUDA,Metal,etc.)"]
end
subgraph hosta[Host A]
srva[rpc-server]<-->backend["Backend (CUDA,Metal,etc.)"]
end
subgraph host[Main Host]
local["Backend (CUDA,Metal,etc.)"]<-->ggml[llama-cli]
ggml[llama-cli]<-->rpcb[RPC backend]
end
style hostn stroke:#66,stroke-width:2px,stroke-dasharray: 5 5
```
Each host can run a different backend, e.g. one with CUDA and another with Metal.
You can also run multiple `rpc-server` instances on the same host, each with a different backend.
## Usage
On each host, build the corresponding backend with `cmake` and add `-DGGML_RPC=ON` to the build options.
For example, to build the CUDA backend with RPC support:
```bash
mkdir build-rpc-cuda
cd build-rpc-cuda
cmake .. -DGGML_CUDA=ON -DGGML_RPC=ON
cmake --build . --config Release
```
Then, start the `rpc-server` with the backend:
```bash
$ bin/rpc-server -p 50052
create_backend: using CUDA backend
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA T1200 Laptop GPU, compute capability 7.5, VMM: yes
Starting RPC server on 0.0.0.0:50052
```
When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.:
```bash
$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052
```
This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device.
On the main host build `llama.cpp` for the local backend and add `-DGGML_RPC=ON` to the build options.
Finally, when running `llama-cli`, use the `--rpc` option to specify the host and port of each `rpc-server`:
```bash
$ bin/llama-cli -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99
```
This way you can offload model layers to both local and remote devices.
### Local cache
The RPC server can use a local cache to store large tensors and avoid transferring them over the network.
This can speed up model loading significantly, especially when using large models.
To enable the cache, use the `-c` option:
```bash
$ bin/rpc-server -c
```
By default, the cache is stored in the `$HOME/.cache/llama.cpp/rpc` directory and can be controlled via the `LLAMA_CACHE` environment variable.

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tools/rpc/rpc-server.cpp Normal file
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#if defined(_MSC_VER)
#define _SILENCE_CXX17_CODECVT_HEADER_DEPRECATION_WARNING
#endif
#include "ggml-cpu.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#ifdef GGML_USE_VULKAN
#include "ggml-vulkan.h"
#endif
#ifdef GGML_USE_SYCL
#include "ggml-sycl.h"
#endif
#include "ggml-rpc.h"
#ifdef _WIN32
# define NOMINMAX
# define DIRECTORY_SEPARATOR '\\'
# include <locale>
# include <windows.h>
# include <fcntl.h>
# include <io.h>
#else
# define DIRECTORY_SEPARATOR '/'
# include <unistd.h>
# include <sys/stat.h>
#endif
#include <codecvt>
#include <string>
#include <stdio.h>
#include <vector>
#include <filesystem>
#include <algorithm>
#include <thread>
namespace fs = std::filesystem;
// NOTE: this is copied from common.cpp to avoid linking with libcommon
// returns true if successful, false otherwise
static bool fs_create_directory_with_parents(const std::string & path) {
#ifdef _WIN32
std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
std::wstring wpath = converter.from_bytes(path);
// if the path already exists, check whether it's a directory
const DWORD attributes = GetFileAttributesW(wpath.c_str());
if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return true;
}
size_t pos_slash = 0;
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
const std::wstring subpath = wpath.substr(0, pos_slash);
const wchar_t * test = subpath.c_str();
const bool success = CreateDirectoryW(test, NULL);
if (!success) {
const DWORD error = GetLastError();
// if the path already exists, ensure that it's a directory
if (error == ERROR_ALREADY_EXISTS) {
const DWORD attributes = GetFileAttributesW(subpath.c_str());
if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
return false;
}
} else {
return false;
}
}
pos_slash += 1;
}
return true;
#else
// if the path already exists, check whether it's a directory
struct stat info;
if (stat(path.c_str(), &info) == 0) {
return S_ISDIR(info.st_mode);
}
size_t pos_slash = 1; // skip leading slashes for directory creation
// process path from front to back, procedurally creating directories
while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
const std::string subpath = path.substr(0, pos_slash);
struct stat info;
// if the path already exists, ensure that it's a directory
if (stat(subpath.c_str(), &info) == 0) {
if (!S_ISDIR(info.st_mode)) {
return false;
}
} else {
// create parent directories
const int ret = mkdir(subpath.c_str(), 0755);
if (ret != 0) {
return false;
}
}
pos_slash += 1;
}
return true;
#endif // _WIN32
}
// NOTE: this is copied from common.cpp to avoid linking with libcommon
static std::string fs_get_cache_directory() {
std::string cache_directory = "";
auto ensure_trailing_slash = [](std::string p) {
// Make sure to add trailing slash
if (p.back() != DIRECTORY_SEPARATOR) {
p += DIRECTORY_SEPARATOR;
}
return p;
};
if (getenv("LLAMA_CACHE")) {
cache_directory = std::getenv("LLAMA_CACHE");
} else {
#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX)
if (std::getenv("XDG_CACHE_HOME")) {
cache_directory = std::getenv("XDG_CACHE_HOME");
} else {
cache_directory = std::getenv("HOME") + std::string("/.cache/");
}
#elif defined(__APPLE__)
cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
#elif defined(_WIN32)
cache_directory = std::getenv("LOCALAPPDATA");
#else
# error Unknown architecture
#endif
cache_directory = ensure_trailing_slash(cache_directory);
cache_directory += "llama.cpp";
}
return ensure_trailing_slash(cache_directory);
}
struct rpc_server_params {
std::string host = "127.0.0.1";
int port = 50052;
size_t backend_mem = 0;
bool use_cache = false;
int n_threads = std::max(1U, std::thread::hardware_concurrency()/2);
};
static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) {
fprintf(stderr, "Usage: %s [options]\n\n", argv[0]);
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help show this help message and exit\n");
fprintf(stderr, " -t, --threads number of threads for the CPU backend (default: %d)\n", params.n_threads);
fprintf(stderr, " -H HOST, --host HOST host to bind to (default: %s)\n", params.host.c_str());
fprintf(stderr, " -p PORT, --port PORT port to bind to (default: %d)\n", params.port);
fprintf(stderr, " -m MEM, --mem MEM backend memory size (in MB)\n");
fprintf(stderr, " -c, --cache enable local file cache\n");
fprintf(stderr, "\n");
}
static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & params) {
std::string arg;
for (int i = 1; i < argc; i++) {
arg = argv[i];
if (arg == "-H" || arg == "--host") {
if (++i >= argc) {
return false;
}
params.host = argv[i];
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
return false;
}
params.n_threads = std::stoi(argv[i]);
if (params.n_threads <= 0) {
fprintf(stderr, "error: invalid number of threads: %d\n", params.n_threads);
return false;
}
} else if (arg == "-p" || arg == "--port") {
if (++i >= argc) {
return false;
}
params.port = std::stoi(argv[i]);
if (params.port <= 0 || params.port > 65535) {
return false;
}
} else if (arg == "-c" || arg == "--cache") {
params.use_cache = true;
} else if (arg == "-m" || arg == "--mem") {
if (++i >= argc) {
return false;
}
params.backend_mem = std::stoul(argv[i]) * 1024 * 1024;
} else if (arg == "-h" || arg == "--help") {
print_usage(argc, argv, params);
exit(0);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
print_usage(argc, argv, params);
exit(0);
}
}
return true;
}
static ggml_backend_t create_backend(const rpc_server_params & params) {
ggml_backend_t backend = NULL;
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#elif GGML_USE_METAL
fprintf(stderr, "%s: using Metal backend\n", __func__);
backend = ggml_backend_metal_init();
if (!backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
#elif GGML_USE_VULKAN
fprintf(stderr, "%s: using Vulkan backend\n", __func__);
backend = ggml_backend_vk_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_vulkan_init() failed\n", __func__);
}
#elif GGML_USE_SYCL
fprintf(stderr, "%s: using SYCL backend\n", __func__);
backend = ggml_backend_sycl_init(0); // init device 0
if (!backend) {
fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__);
}
#endif
// if there aren't GPU Backends fallback to CPU backend
if (!backend) {
fprintf(stderr, "%s: using CPU backend\n", __func__);
backend = ggml_backend_cpu_init();
ggml_backend_cpu_set_n_threads(backend, params.n_threads);
}
return backend;
}
static void get_backend_memory(size_t * free_mem, size_t * total_mem) {
#ifdef GGML_USE_CUDA
ggml_backend_cuda_get_device_memory(0, free_mem, total_mem);
#elif GGML_USE_VULKAN
ggml_backend_vk_get_device_memory(0, free_mem, total_mem);
#elif GGML_USE_SYCL
ggml_backend_sycl_get_device_memory(0, free_mem, total_mem);
#else
#ifdef _WIN32
MEMORYSTATUSEX status;
status.dwLength = sizeof(status);
GlobalMemoryStatusEx(&status);
*total_mem = status.ullTotalPhys;
*free_mem = status.ullAvailPhys;
#else
long pages = sysconf(_SC_PHYS_PAGES);
long page_size = sysconf(_SC_PAGE_SIZE);
*total_mem = pages * page_size;
*free_mem = *total_mem;
#endif
#endif
}
int main(int argc, char * argv[]) {
rpc_server_params params;
if (!rpc_server_params_parse(argc, argv, params)) {
fprintf(stderr, "Invalid parameters\n");
return 1;
}
if (params.host != "127.0.0.1") {
fprintf(stderr, "\n");
fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
fprintf(stderr, "WARNING: Host ('%s') is != '127.0.0.1'\n", params.host.c_str());
fprintf(stderr, " Never expose the RPC server to an open network!\n");
fprintf(stderr, " This is an experimental feature and is not secure!\n");
fprintf(stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n");
fprintf(stderr, "\n");
}
ggml_backend_t backend = create_backend(params);
if (!backend) {
fprintf(stderr, "Failed to create backend\n");
return 1;
}
std::string endpoint = params.host + ":" + std::to_string(params.port);
size_t free_mem, total_mem;
if (params.backend_mem > 0) {
free_mem = params.backend_mem;
total_mem = params.backend_mem;
} else {
get_backend_memory(&free_mem, &total_mem);
}
const char * cache_dir = nullptr;
std::string cache_dir_str;
if (params.use_cache) {
cache_dir_str = fs_get_cache_directory() + "rpc/";
if (!fs_create_directory_with_parents(cache_dir_str)) {
fprintf(stderr, "Failed to create cache directory: %s\n", cache_dir_str.c_str());
return 1;
}
cache_dir = cache_dir_str.c_str();
}
printf("Starting RPC server v%d.%d.%d\n",
RPC_PROTO_MAJOR_VERSION,
RPC_PROTO_MINOR_VERSION,
RPC_PROTO_PATCH_VERSION);
printf(" endpoint : %s\n", endpoint.c_str());
printf(" local cache : %s\n", cache_dir ? cache_dir : "n/a");
printf(" backend memory : %zu MB\n", free_mem / (1024 * 1024));
ggml_backend_rpc_start_server(backend, endpoint.c_str(), cache_dir, free_mem, total_mem);
ggml_backend_free(backend);
return 0;
}

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set(TARGET llama-run)
add_executable(${TARGET} run.cpp linenoise.cpp/linenoise.cpp)
# TODO: avoid copying this code block from common/CMakeLists.txt
set(LLAMA_RUN_EXTRA_LIBS "")
if (LLAMA_CURL)
find_package(CURL REQUIRED)
target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_CURL)
include_directories(${CURL_INCLUDE_DIRS})
find_library(CURL_LIBRARY curl REQUIRED)
set(LLAMA_RUN_EXTRA_LIBS ${LLAMA_RUN_EXTRA_LIBS} ${CURL_LIBRARY})
endif ()
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT} ${LLAMA_RUN_EXTRA_LIBS})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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# llama.cpp/example/run
The purpose of this example is to demonstrate a minimal usage of llama.cpp for running models.
```bash
llama-run granite3-moe
```
```bash
Description:
Runs a llm
Usage:
llama-run [options] model [prompt]
Options:
-c, --context-size <value>
Context size (default: 2048)
-n, -ngl, --ngl <value>
Number of GPU layers (default: 0)
--temp <value>
Temperature (default: 0.8)
-v, --verbose, --log-verbose
Set verbosity level to infinity (i.e. log all messages, useful for debugging)
-h, --help
Show help message
Commands:
model
Model is a string with an optional prefix of
huggingface:// (hf://), ollama://, https:// or file://.
If no protocol is specified and a file exists in the specified
path, file:// is assumed, otherwise if a file does not exist in
the specified path, ollama:// is assumed. Models that are being
pulled are downloaded with .partial extension while being
downloaded and then renamed as the file without the .partial
extension when complete.
Examples:
llama-run llama3
llama-run ollama://granite-code
llama-run ollama://smollm:135m
llama-run hf://QuantFactory/SmolLM-135M-GGUF/SmolLM-135M.Q2_K.gguf
llama-run huggingface://bartowski/SmolLM-1.7B-Instruct-v0.2-GGUF/SmolLM-1.7B-Instruct-v0.2-IQ3_M.gguf
llama-run https://example.com/some-file1.gguf
llama-run some-file2.gguf
llama-run file://some-file3.gguf
llama-run --ngl 999 some-file4.gguf
llama-run --ngl 999 some-file5.gguf Hello World
```

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/* linenoise.h -- VERSION 1.0
*
* Guerrilla line editing library against the idea that a line editing lib
* needs to be 20,000 lines of C++ code.
*
* See linenoise.cpp for more information.
*
* ------------------------------------------------------------------------
*
* Copyright (c) 2010-2023, Salvatore Sanfilippo <antirez at gmail dot com>
* Copyright (c) 2010-2013, Pieter Noordhuis <pcnoordhuis at gmail dot com>
* Copyright (c) 2025, Eric Curtin <ericcurtin17 at gmail dot com>
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are
* met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
* HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef __LINENOISE_H
#define __LINENOISE_H
#ifdef __cplusplus
extern "C" {
#endif
#include <stddef.h> /* For size_t. */
#include <stdlib.h>
extern const char * linenoiseEditMore;
/* The linenoiseState structure represents the state during line editing.
* We pass this state to functions implementing specific editing
* functionalities. */
struct linenoiseState {
int in_completion; /* The user pressed TAB and we are now in completion
* mode, so input is handled by completeLine(). */
size_t completion_idx; /* Index of next completion to propose. */
int ifd; /* Terminal stdin file descriptor. */
int ofd; /* Terminal stdout file descriptor. */
char * buf; /* Edited line buffer. */
size_t buflen; /* Edited line buffer size. */
const char * prompt; /* Prompt to display. */
size_t plen; /* Prompt length. */
size_t pos; /* Current cursor position. */
size_t oldcolpos; /* Previous refresh cursor column position. */
size_t len; /* Current edited line length. */
size_t cols; /* Number of columns in terminal. */
size_t oldrows; /* Rows used by last refreshed line (multiline mode) */
int history_index; /* The history index we are currently editing. */
};
struct linenoiseCompletions {
size_t len = 0;
char ** cvec = nullptr;
bool to_free = true;
~linenoiseCompletions() {
if (!to_free) {
return;
}
for (size_t i = 0; i < len; ++i) {
free(cvec[i]);
}
free(cvec);
}
};
/* Non blocking API. */
int linenoiseEditStart(struct linenoiseState * l, int stdin_fd, int stdout_fd, char * buf, size_t buflen,
const char * prompt);
const char * linenoiseEditFeed(struct linenoiseState * l);
void linenoiseEditStop(struct linenoiseState * l);
void linenoiseHide(struct linenoiseState * l);
void linenoiseShow(struct linenoiseState * l);
/* Blocking API. */
const char * linenoise(const char * prompt);
void linenoiseFree(void * ptr);
/* Completion API. */
typedef void(linenoiseCompletionCallback)(const char *, linenoiseCompletions *);
typedef const char *(linenoiseHintsCallback) (const char *, int * color, int * bold);
typedef void(linenoiseFreeHintsCallback)(const char *);
void linenoiseSetCompletionCallback(linenoiseCompletionCallback *);
void linenoiseSetHintsCallback(linenoiseHintsCallback *);
void linenoiseSetFreeHintsCallback(linenoiseFreeHintsCallback *);
void linenoiseAddCompletion(linenoiseCompletions *, const char *);
/* History API. */
int linenoiseHistoryAdd(const char * line);
int linenoiseHistorySetMaxLen(int len);
int linenoiseHistorySave(const char * filename);
int linenoiseHistoryLoad(const char * filename);
/* Other utilities. */
void linenoiseClearScreen(void);
void linenoiseSetMultiLine(int ml);
void linenoisePrintKeyCodes(void);
void linenoiseMaskModeEnable(void);
void linenoiseMaskModeDisable(void);
/* Encoding functions. */
typedef size_t(linenoisePrevCharLen)(const char * buf, size_t buf_len, size_t pos, size_t * col_len);
typedef size_t(linenoiseNextCharLen)(const char * buf, size_t buf_len, size_t pos, size_t * col_len);
typedef size_t(linenoiseReadCode)(int fd, char * buf, size_t buf_len, int * c);
void linenoiseSetEncodingFunctions(linenoisePrevCharLen * prevCharLenFunc, linenoiseNextCharLen * nextCharLenFunc,
linenoiseReadCode * readCodeFunc);
#ifdef __cplusplus
}
#endif
#endif /* __LINENOISE_H */

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set(TARGET llama-server)
option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
if (MINGW)
# fix: https://github.com/ggml-org/llama.cpp/actions/runs/9651004652/job/26617901362?pr=8006
add_compile_definitions(_WIN32_WINNT=${GGML_WIN_VER})
endif()
set(TARGET_SRCS
server.cpp
utils.hpp
httplib.h
)
set(PUBLIC_ASSETS
index.html.gz
loading.html
)
foreach(asset ${PUBLIC_ASSETS})
set(input "${CMAKE_CURRENT_SOURCE_DIR}/public/${asset}")
set(output "${CMAKE_CURRENT_BINARY_DIR}/${asset}.hpp")
list(APPEND TARGET_SRCS ${output})
add_custom_command(
DEPENDS "${input}"
OUTPUT "${output}"
COMMAND "${CMAKE_COMMAND}" "-DINPUT=${input}" "-DOUTPUT=${output}" -P "${PROJECT_SOURCE_DIR}/scripts/xxd.cmake"
)
set_source_files_properties(${output} PROPERTIES GENERATED TRUE)
endforeach()
add_executable(${TARGET} ${TARGET_SRCS})
install(TARGETS ${TARGET} RUNTIME)
target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
if (LLAMA_SERVER_SSL)
find_package(OpenSSL REQUIRED)
target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
endif()
if (WIN32)
TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
endif()
target_compile_features(${TARGET} PRIVATE cxx_std_17)

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### Server benchmark tools
Benchmark is using [k6](https://k6.io/).
##### Install k6 and sse extension
SSE is not supported by default in k6, you have to build k6 with the [xk6-sse](https://github.com/phymbert/xk6-sse) extension.
Example (assuming golang >= 1.21 is installed):
```shell
go install go.k6.io/xk6/cmd/xk6@latest
$GOPATH/bin/xk6 build master \
--with github.com/phymbert/xk6-sse
```
#### Download a dataset
This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
```shell
wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
```
#### Download a model
Example for PHI-2
```shell
../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
```
#### Start the server
The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
Example:
```shell
llama-server --host localhost --port 8080 \
--model ggml-model-q4_0.gguf \
--cont-batching \
--metrics \
--parallel 8 \
--batch-size 512 \
--ctx-size 4096 \
-ngl 33
```
#### Run the benchmark
For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
```shell
./k6 run script.js --duration 10m --iterations 500 --vus 8
```
The benchmark values can be overridden with:
- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
- `SERVER_BENCH_DATASET` path to the benchmark dataset file
- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
Note: the local tokenizer is just a string space split, real number of tokens will differ.
Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
```shell
SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
```
To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
#### Metrics
Following metrics are available computed from the OAI chat completions response `usage`:
- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
K6 metrics might be compared against [server metrics](../README.md), with:
```shell
curl http://localhost:8080/metrics
```
### Using the CI python script
The `bench.py` script does several steps:
- start the server
- define good variable for k6
- run k6 script
- extract metrics from prometheus
It aims to be used in the CI, but you can run it manually:
```shell
LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/llama-server python bench.py \
--runner-label local \
--name local \
--branch `git rev-parse --abbrev-ref HEAD` \
--commit `git rev-parse HEAD` \
--scenario script.js \
--duration 5m \
--hf-repo ggml-org/models \
--hf-file phi-2/ggml-model-q4_0.gguf \
--model-path-prefix models \
--parallel 4 \
-ngl 33 \
--batch-size 2048 \
--ubatch-size 256 \
--ctx-size 4096 \
--n-prompts 200 \
--max-prompt-tokens 256 \
--max-tokens 256
```

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from __future__ import annotations
import argparse
import json
import os
import re
import signal
import socket
import subprocess
import sys
import threading
import time
import traceback
from contextlib import closing
from datetime import datetime
import matplotlib
import matplotlib.dates
import matplotlib.pyplot as plt
import requests
from statistics import mean
def main(args_in: list[str] | None = None) -> None:
parser = argparse.ArgumentParser(description="Start server benchmark scenario")
parser.add_argument("--name", type=str, help="Bench name", required=True)
parser.add_argument("--runner-label", type=str, help="Runner label", required=True)
parser.add_argument("--branch", type=str, help="Branch name", default="detached")
parser.add_argument("--commit", type=str, help="Commit name", default="dirty")
parser.add_argument("--host", type=str, help="Server listen host", default="0.0.0.0")
parser.add_argument("--port", type=int, help="Server listen host", default="8080")
parser.add_argument("--model-path-prefix", type=str, help="Prefix where to store the model files", default="models")
parser.add_argument("--n-prompts", type=int,
help="SERVER_BENCH_N_PROMPTS: total prompts to randomly select in the benchmark", required=True)
parser.add_argument("--max-prompt-tokens", type=int,
help="SERVER_BENCH_MAX_PROMPT_TOKENS: maximum prompt tokens to filter out in the dataset",
required=True)
parser.add_argument("--max-tokens", type=int,
help="SERVER_BENCH_MAX_CONTEXT: maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens",
required=True)
parser.add_argument("--hf-repo", type=str, help="Hugging Face model repository", required=True)
parser.add_argument("--hf-file", type=str, help="Hugging Face model file", required=True)
parser.add_argument("-ngl", "--n-gpu-layers", type=int, help="layers to the GPU for computation", required=True)
parser.add_argument("--ctx-size", type=int, help="Set the size of the prompt context", required=True)
parser.add_argument("--parallel", type=int, help="Set the number of slots for process requests", required=True)
parser.add_argument("--batch-size", type=int, help="Set the batch size for prompt processing", required=True)
parser.add_argument("--ubatch-size", type=int, help="physical maximum batch size", required=True)
parser.add_argument("--scenario", type=str, help="Scenario to run", required=True)
parser.add_argument("--duration", type=str, help="Bench scenario", required=True)
args = parser.parse_args(args_in)
start_time = time.time()
# Start the server and performance scenario
try:
server_process = start_server(args)
except Exception:
print("bench: server start error :")
traceback.print_exc(file=sys.stdout)
sys.exit(1)
# start the benchmark
iterations = 0
data = {}
try:
start_benchmark(args)
with open("results.github.env", 'w') as github_env:
# parse output
with open('k6-results.json', 'r') as bench_results:
# Load JSON data from file
data = json.load(bench_results)
for metric_name in data['metrics']:
for metric_metric in data['metrics'][metric_name]:
value = data['metrics'][metric_name][metric_metric]
if isinstance(value, float) or isinstance(value, int):
value = round(value, 2)
data['metrics'][metric_name][metric_metric]=value
github_env.write(
f"{escape_metric_name(metric_name)}_{escape_metric_name(metric_metric)}={value}\n")
iterations = data['root_group']['checks']['success completion']['passes']
except Exception:
print("bench: error :")
traceback.print_exc(file=sys.stdout)
# Stop the server
if server_process:
try:
print(f"bench: shutting down server pid={server_process.pid} ...")
if os.name == 'nt':
interrupt = signal.CTRL_C_EVENT
else:
interrupt = signal.SIGINT
server_process.send_signal(interrupt)
server_process.wait(0.5)
except subprocess.TimeoutExpired:
print(f"server still alive after 500ms, force-killing pid={server_process.pid} ...")
server_process.kill() # SIGKILL
server_process.wait()
while is_server_listening(args.host, args.port):
time.sleep(0.1)
title = (f"llama.cpp {args.name} on {args.runner_label}\n "
f"duration={args.duration} {iterations} iterations")
xlabel = (f"{args.hf_repo}/{args.hf_file}\n"
f"parallel={args.parallel} ctx-size={args.ctx_size} ngl={args.n_gpu_layers} batch-size={args.batch_size} ubatch-size={args.ubatch_size} pp={args.max_prompt_tokens} pp+tg={args.max_tokens}\n"
f"branch={args.branch} commit={args.commit}")
# Prometheus
end_time = time.time()
prometheus_metrics = {}
if is_server_listening("0.0.0.0", 9090):
metrics = ['prompt_tokens_seconds', 'predicted_tokens_seconds',
'kv_cache_usage_ratio', 'requests_processing', 'requests_deferred']
for metric in metrics:
resp = requests.get(f"http://localhost:9090/api/v1/query_range",
params={'query': 'llamacpp:' + metric, 'start': start_time, 'end': end_time, 'step': 2})
with open(f"{metric}.json", 'w') as metric_json:
metric_json.write(resp.text)
if resp.status_code != 200:
print(f"bench: unable to extract prometheus metric {metric}: {resp.text}")
else:
metric_data = resp.json()
values = metric_data['data']['result'][0]['values']
timestamps, metric_values = zip(*values)
metric_values = [float(value) for value in metric_values]
prometheus_metrics[metric] = metric_values
timestamps_dt = [str(datetime.fromtimestamp(int(ts))) for ts in timestamps]
plt.figure(figsize=(16, 10), dpi=80)
plt.plot(timestamps_dt, metric_values, label=metric)
plt.xticks(rotation=0, fontsize=14, horizontalalignment='center', alpha=.7)
plt.yticks(fontsize=12, alpha=.7)
ylabel = f"llamacpp:{metric}"
plt.title(title,
fontsize=14, wrap=True)
plt.grid(axis='both', alpha=.3)
plt.ylabel(ylabel, fontsize=22)
plt.xlabel(xlabel, fontsize=14, wrap=True)
plt.gca().xaxis.set_major_locator(matplotlib.dates.MinuteLocator())
plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter("%Y-%m-%d %H:%M:%S"))
plt.gcf().autofmt_xdate()
# Remove borders
plt.gca().spines["top"].set_alpha(0.0)
plt.gca().spines["bottom"].set_alpha(0.3)
plt.gca().spines["right"].set_alpha(0.0)
plt.gca().spines["left"].set_alpha(0.3)
# Save the plot as a jpg image
plt.savefig(f'{metric}.jpg', dpi=60)
plt.close()
# Mermaid format in case images upload failed
with open(f"{metric}.mermaid", 'w') as mermaid_f:
mermaid = (
f"""---
config:
xyChart:
titleFontSize: 12
width: 900
height: 600
themeVariables:
xyChart:
titleColor: "#000000"
---
xychart-beta
title "{title}"
y-axis "llamacpp:{metric}"
x-axis "llamacpp:{metric}" {int(min(timestamps))} --> {int(max(timestamps))}
line [{', '.join([str(round(float(value), 2)) for value in metric_values])}]
""")
mermaid_f.write(mermaid)
# 140 chars max for commit status description
bench_results = {
"i": iterations,
"req": {
"p95": round(data['metrics']["http_req_duration"]["p(95)"], 2),
"avg": round(data['metrics']["http_req_duration"]["avg"], 2),
},
"pp": {
"p95": round(data['metrics']["llamacpp_prompt_processing_second"]["p(95)"], 2),
"avg": round(data['metrics']["llamacpp_prompt_processing_second"]["avg"], 2),
"0": round(mean(prometheus_metrics['prompt_tokens_seconds']), 2) if 'prompt_tokens_seconds' in prometheus_metrics else 0,
},
"tg": {
"p95": round(data['metrics']["llamacpp_tokens_second"]["p(95)"], 2),
"avg": round(data['metrics']["llamacpp_tokens_second"]["avg"], 2),
"0": round(mean(prometheus_metrics['predicted_tokens_seconds']), 2) if 'predicted_tokens_seconds' in prometheus_metrics else 0,
},
}
with open("results.github.env", 'a') as github_env:
github_env.write(f"BENCH_RESULTS={json.dumps(bench_results, indent=None, separators=(',', ':') )}\n")
github_env.write(f"BENCH_ITERATIONS={iterations}\n")
title = title.replace('\n', ' ')
xlabel = xlabel.replace('\n', ' ')
github_env.write(f"BENCH_GRAPH_TITLE={title}\n")
github_env.write(f"BENCH_GRAPH_XLABEL={xlabel}\n")
def start_benchmark(args):
k6_path = './k6'
if 'BENCH_K6_BIN_PATH' in os.environ:
k6_path = os.environ['BENCH_K6_BIN_PATH']
k6_args = [
'run', args.scenario,
'--no-color',
'--no-connection-reuse',
'--no-vu-connection-reuse',
]
k6_args.extend(['--duration', args.duration])
k6_args.extend(['--iterations', args.n_prompts])
k6_args.extend(['--vus', args.parallel])
k6_args.extend(['--summary-export', 'k6-results.json'])
k6_args.extend(['--out', 'csv=k6-results.csv'])
args = f"SERVER_BENCH_N_PROMPTS={args.n_prompts} SERVER_BENCH_MAX_PROMPT_TOKENS={args.max_prompt_tokens} SERVER_BENCH_MAX_CONTEXT={args.max_tokens} "
args = args + ' '.join([str(arg) for arg in [k6_path, *k6_args]])
print(f"bench: starting k6 with: {args}")
k6_completed = subprocess.run(args, shell=True, stdout=sys.stdout, stderr=sys.stderr)
if k6_completed.returncode != 0:
raise Exception("bench: unable to run k6")
def start_server(args):
server_process = start_server_background(args)
attempts = 0
max_attempts = 600
if 'GITHUB_ACTIONS' in os.environ:
max_attempts *= 2
while not is_server_listening(args.host, args.port):
attempts += 1
if attempts > max_attempts:
assert False, "server not started"
print(f"bench: waiting for server to start ...")
time.sleep(0.5)
attempts = 0
while not is_server_ready(args.host, args.port):
attempts += 1
if attempts > max_attempts:
assert False, "server not ready"
print(f"bench: waiting for server to be ready ...")
time.sleep(0.5)
print("bench: server started and ready.")
return server_process
def start_server_background(args):
# Start the server
server_path = '../../../build/bin/llama-server'
if 'LLAMA_SERVER_BIN_PATH' in os.environ:
server_path = os.environ['LLAMA_SERVER_BIN_PATH']
server_args = [
'--host', args.host,
'--port', args.port,
]
server_args.extend(['--hf-repo', args.hf_repo])
server_args.extend(['--hf-file', args.hf_file])
server_args.extend(['--n-gpu-layers', args.n_gpu_layers])
server_args.extend(['--ctx-size', args.ctx_size])
server_args.extend(['--parallel', args.parallel])
server_args.extend(['--batch-size', args.batch_size])
server_args.extend(['--ubatch-size', args.ubatch_size])
server_args.extend(['--n-predict', args.max_tokens * 2])
server_args.extend(['--defrag-thold', "0.1"])
server_args.append('--cont-batching')
server_args.append('--metrics')
server_args.append('--flash-attn')
args = [str(arg) for arg in [server_path, *server_args]]
print(f"bench: starting server with: {' '.join(args)}")
pkwargs = {
'stdout': subprocess.PIPE,
'stderr': subprocess.PIPE
}
server_process = subprocess.Popen(
args,
**pkwargs) # pyright: ignore[reportArgumentType, reportCallIssue]
def server_log(in_stream, out_stream):
for line in iter(in_stream.readline, b''):
print(line.decode('utf-8'), end='', file=out_stream)
thread_stdout = threading.Thread(target=server_log, args=(server_process.stdout, sys.stdout))
thread_stdout.start()
thread_stderr = threading.Thread(target=server_log, args=(server_process.stderr, sys.stderr))
thread_stderr.start()
return server_process
def is_server_listening(server_fqdn, server_port):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
result = sock.connect_ex((server_fqdn, server_port))
_is_server_listening = result == 0
if _is_server_listening:
print(f"server is listening on {server_fqdn}:{server_port}...")
return _is_server_listening
def is_server_ready(server_fqdn, server_port):
url = f"http://{server_fqdn}:{server_port}/health"
response = requests.get(url)
return response.status_code == 200
def escape_metric_name(metric_name):
return re.sub('[^A-Z0-9]', '_', metric_name.upper())
if __name__ == '__main__':
main()

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global:
scrape_interval: 10s
external_labels:
llamacpp: 'server'
scrape_configs:
- job_name: 'llama.cpp server'
static_configs:
- targets: ['localhost:8080']

2
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matplotlib
requests

162
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import sse from 'k6/x/sse'
import {check, sleep} from 'k6'
import {SharedArray} from 'k6/data'
import {Counter, Rate, Trend} from 'k6/metrics'
import exec from 'k6/execution';
// Server chat completions prefix
const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
// Model name to request
const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
// Dataset path
const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
// Max tokens to predict
const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
// Max prompt tokens
const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
// Max slot context
const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
export function setup() {
console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
}
const data = new SharedArray('conversations', function () {
const tokenizer = (message) => message.split(/[\s,'".?]/)
return JSON.parse(open(dataset_path))
// Filter out the conversations with less than 2 turns.
.filter(data => data["conversations"].length >= 2)
.filter(data => data["conversations"][0]["from"] === "human")
.map(data => {
return {
prompt: data["conversations"][0]["value"],
n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
}
})
// Filter out too short sequences
.filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
// Filter out too long sequences.
.filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
// Keep only first n prompts
.slice(0, n_prompt)
})
const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
const llamacpp_prompt_processing_second = new Trend('llamacpp_prompt_processing_second')
const llamacpp_emit_first_token_second = new Trend('llamacpp_emit_first_token_second')
const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
export const options = {
thresholds: {
llamacpp_completions_truncated_rate: [
// more than 80% of truncated input will abort the test
{threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
],
},
duration: '10m',
vus: 8,
}
export default function () {
const conversation = data[exec.scenario.iterationInInstance % data.length]
const payload = {
"messages": [
{
"role": "system",
"content": "You are ChatGPT, an AI assistant.",
},
{
"role": "user",
"content": conversation.prompt,
}
],
"model": model,
"stream": true,
"stream_options": {
"include_usage": true, // False to be supported in llama.cpp server
},
"seed": 42,
"max_tokens": max_tokens,
"stop": ["<|im_end|>"] // This is temporary for phi-2 base (i.e. not instructed) since the server expects that the model always to emit BOS
}
const params = {method: 'POST', body: JSON.stringify(payload)};
const startTime = new Date()
let promptEvalEndTime = null
let prompt_tokens = 0
let completions_tokens = 0
let finish_reason = null
const res = sse.open(`${server_url}/chat/completions`, params, function (client) {
client.on('event', function (event) {
if (promptEvalEndTime == null) {
promptEvalEndTime = new Date()
llamacpp_emit_first_token_second.add((promptEvalEndTime - startTime) / 1.e3)
}
if (event.data === '[DONE]' || event.data === '') {
return
}
let chunk = JSON.parse(event.data)
if (chunk.choices && chunk.choices.length > 0) {
let choice = chunk.choices[0]
if (choice.finish_reason) {
finish_reason = choice.finish_reason
}
}
if (chunk.usage) {
prompt_tokens = chunk.usage.prompt_tokens
llamacpp_prompt_tokens.add(prompt_tokens)
llamacpp_prompt_tokens_total_counter.add(prompt_tokens)
completions_tokens = chunk.usage.completion_tokens
llamacpp_completion_tokens.add(completions_tokens)
llamacpp_completion_tokens_total_counter.add(completions_tokens)
}
})
client.on('error', function (e) {
console.log('An unexpected error occurred: ', e.error());
throw e;
})
})
check(res, {'success completion': (r) => r.status === 200})
const endTime = new Date()
const promptEvalTime = promptEvalEndTime - startTime
if (promptEvalTime > 0) {
llamacpp_prompt_processing_second.add(prompt_tokens / (promptEvalEndTime - startTime) * 1.e3)
}
const completion_time = endTime - promptEvalEndTime
if (completions_tokens > 0 && completion_time > 0) {
llamacpp_tokens_second.add(completions_tokens / completion_time * 1.e3)
}
llamacpp_completions_truncated_rate.add(finish_reason === 'length')
llamacpp_completions_stop_rate.add(finish_reason === 'stop')
sleep(0.3)
}

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#!/bin/bash
API_URL="${API_URL:-http://127.0.0.1:8080}"
CHAT=(
"Hello, Assistant."
"Hello. How may I help you today?"
)
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
trim() {
shopt -s extglob
set -- "${1##+([[:space:]])}"
printf "%s" "${1%%+([[:space:]])}"
}
trim_trailing() {
shopt -s extglob
printf "%s" "${1%%+([[:space:]])}"
}
format_prompt() {
if [[ "${#CHAT[@]}" -eq 0 ]]; then
echo -n "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>"
else
LAST_INDEX=$(( ${#CHAT[@]} - 1 ))
echo -n "${CHAT[$LAST_INDEX]}\n[INST] $1 [/INST]"
fi
}
tokenize() {
curl \
--silent \
--request POST \
--url "${API_URL}/tokenize" \
--header "Content-Type: application/json" \
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
| jq '.tokens[]'
}
N_KEEP=$(tokenize "[INST] <<SYS>>\n${INSTRUCTION}\n<</SYS>>" | wc -l)
chat_completion() {
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
prompt: .,
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: $n_keep,
n_predict: 1024,
stop: ["[INST]"],
stream: true
}')"
# Create a temporary file to hold the Python output
TEMPFILE=$(mktemp)
exec 3< <(curl \
--silent \
--no-buffer \
--request POST \
--url "${API_URL}/completion" \
--header "Content-Type: application/json" \
--data-raw "${DATA}")
python -c "
import json
import sys
answer = ''
while True:
line = sys.stdin.readline()
if not line:
break
if line.startswith('data: '):
json_content = line[6:].strip()
content = json.loads(json_content)['content']
sys.stdout.write(content)
sys.stdout.flush()
answer += content
answer = answer.rstrip('\n')
# Write the answer to the temporary file
with open('$TEMPFILE', 'w') as f:
f.write(answer)
" <&3
exec 3<&-
# Read the answer from the temporary file
ANSWER=$(cat $TEMPFILE)
# Clean up the temporary file
rm $TEMPFILE
printf "\n"
CHAT+=("$1" "$(trim "$ANSWER")")
}
while true; do
echo -en "\033[0;32m" # Green color
read -r -e -p "> " QUESTION
echo -en "\033[0m" # Reset color
chat_completion "${QUESTION}"
done

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import * as readline from 'node:readline'
import { stdin, stdout } from 'node:process'
import { readFileSync } from 'node:fs'
import { SchemaConverter } from './public_legacy/json-schema-to-grammar.mjs'
const args = process.argv.slice(2);
const grammarJsonSchemaFile = args.find(
(_, index) => args[index - 1] === "--grammar-json-schema"
);
const no_cached_prompt = args.find(
(_, index) => args[index - 1] === "--no-cache-prompt"
) ?? "false";
const grammarFile = args.find((_, index) => args[index - 1] === "--grammar");
// Example usage: function,arguments
const grammarJsonSchemaPropOrder = args.find(
(_, index) => args[index - 1] === "--grammar-json-schema-prop-order"
);
const propOrder = grammarJsonSchemaPropOrder
? grammarJsonSchemaPropOrder
.split(",")
.reduce((acc, cur, index) => ({ ...acc, [cur]: index }), {})
: {};
let grammar = null
if (grammarJsonSchemaFile) {
let schema = JSON.parse(readFileSync(grammarJsonSchemaFile, 'utf-8'))
const converter = new SchemaConverter({prop_order: propOrder, allow_fetch: true})
schema = await converter.resolveRefs(schema, grammarJsonSchemaFile)
converter.visit(schema, '')
grammar = converter.formatGrammar()
}
if (grammarFile) {
grammar = readFileSync(grammarFile, 'utf-8')
}
// for cached prompt
let slot_id = -1;
const API_URL = 'http://127.0.0.1:8080'
const chat = [
{
human: "Hello, Assistant.",
assistant: "Hello. How may I help you today?"
},
{
human: "Please tell me the largest city in Europe.",
assistant: "Sure. The largest city in Europe is Moscow, the capital of Russia."
},
]
const instruction = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.`
function format_prompt(question) {
return `${instruction}\n${
chat.map(m =>`### Human: ${m.human}\n### Assistant: ${m.assistant}`).join("\n")
}\n### Human: ${question}\n### Assistant:`
}
async function tokenize(content) {
const result = await fetch(`${API_URL}/tokenize`, {
method: 'POST',
body: JSON.stringify({ content })
})
if (!result.ok) {
return []
}
return await result.json().tokens
}
const n_keep = await tokenize(instruction).length
async function chat_completion(question) {
const result = await fetch(`${API_URL}/completion`, {
method: 'POST',
body: JSON.stringify({
prompt: format_prompt(question),
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: n_keep,
n_predict: 256,
cache_prompt: no_cached_prompt === "false",
slot_id: slot_id,
stop: ["\n### Human:"], // stop completion after generating this
grammar,
stream: true,
})
})
if (!result.ok) {
return
}
let answer = ''
for await (var chunk of result.body) {
const t = Buffer.from(chunk).toString('utf8')
if (t.startsWith('data: ')) {
const message = JSON.parse(t.substring(6))
slot_id = message.slot_id
answer += message.content
process.stdout.write(message.content)
if (message.stop) {
if (message.truncated) {
chat.shift()
}
break
}
}
}
process.stdout.write('\n')
chat.push({ human: question, assistant: answer.trimStart() })
}
const rl = readline.createInterface({ input: stdin, output: stdout });
const readlineQuestion = (rl, query, options) => new Promise((resolve, reject) => {
rl.question(query, options, resolve)
});
while(true) {
const question = await readlineQuestion(rl, '> ')
await chat_completion(question)
}

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#!/bin/bash
API_URL="${API_URL:-http://127.0.0.1:8080}"
CHAT=(
"Hello, Assistant."
"Hello. How may I help you today?"
"Please tell me the largest city in Europe."
"Sure. The largest city in Europe is Moscow, the capital of Russia."
)
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
trim() {
shopt -s extglob
set -- "${1##+([[:space:]])}"
printf "%s" "${1%%+([[:space:]])}"
}
trim_trailing() {
shopt -s extglob
printf "%s" "${1%%+([[:space:]])}"
}
format_prompt() {
echo -n "${INSTRUCTION}"
printf "\n### Human: %s\n### Assistant: %s" "${CHAT[@]}" "$1"
}
tokenize() {
curl \
--silent \
--request POST \
--url "${API_URL}/tokenize" \
--header "Content-Type: application/json" \
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
| jq '.tokens[]'
}
N_KEEP=$(tokenize "${INSTRUCTION}" | wc -l)
chat_completion() {
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
prompt: .,
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: $n_keep,
n_predict: 256,
cache_prompt: true,
stop: ["\n### Human:"],
stream: true
}')"
ANSWER=''
while IFS= read -r LINE; do
if [[ $LINE = data:* ]]; then
CONTENT="$(echo "${LINE:5}" | jq -r '.content')"
printf "%s" "${CONTENT}"
ANSWER+="${CONTENT}"
fi
done < <(curl \
--silent \
--no-buffer \
--request POST \
--url "${API_URL}/completion" \
--header "Content-Type: application/json" \
--data-raw "${DATA}")
printf "\n"
CHAT+=("$1" "$(trim "$ANSWER")")
}
while true; do
read -r -e -p "> " QUESTION
chat_completion "${QUESTION}"
done

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<!DOCTYPE html>
<html>
<head>
<meta http-equiv="refresh" content="5">
</head>
<body>
<div id="loading">
The model is loading. Please wait.<br/>
The user interface will appear soon.
</div>
</body>
</html>

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@import url("theme-snowstorm.css");
@import url("theme-polarnight.css");
@import url("theme-ketivah.css");
@import url("theme-mangotango.css");
@import url("theme-playground.css");
@import url("theme-beeninorder.css");
:root {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(217.5, 26.7%, 94.1%);
--primary-color-1-hue: 217.5;
--primary-color-1-saturation: 26.7%;
--primary-color-1-lightness: 94.1%;
--primary-color-2: hsl(218.2, 26.8%, 92.0%);
--primary-color-2-hue: 218.2;
--primary-color-2-saturation: 26.8%;
--primary-color-2-lightness: 92.0%;
--primary-color-3: hsl(218.8, 27.9%, 88.0%);
--primary-color-3-hue: 218.8;
--primary-color-3-saturation: 27.9%;
--primary-color-3-lightness: 88.0%;
--primary-color-4: hsl(218.8, 18.3%, 81.8%);
--primary-color-4-hue: 218.8;
--primary-color-4-saturation: 18.3%;
--primary-color-4-lightness: 81.8%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(220.0, 16.4%, 21.6%);
--secondary-color-1-hue: 220.0;
--secondary-color-1-saturation: 16.4%;
--secondary-color-1-lightness: 21.6%;
--secondary-color-2: hsl(221.7, 16.3%, 27.6%);
--secondary-color-2-hue: 221.7;
--secondary-color-2-saturation: 16.3%;
--secondary-color-2-lightness: 27.6%;
--secondary-color-3: hsl(220.0, 16.8%, 31.6%);
--secondary-color-3-hue: 220.0;
--secondary-color-3-saturation: 16.8%;
--secondary-color-3-lightness: 31.6%;
--secondary-color-4: hsl(220.0, 16.5%, 35.7%);
--secondary-color-4-hue: 220.0;
--secondary-color-4-saturation: 16.5%;
--secondary-color-4-lightness: 35.7%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
--theme-nuance-color-1-hue: 178.7;
--theme-nuance-color-1-saturation: 25.1%;
--theme-nuance-color-1-lightness: 64.9%;
--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
--theme-nuance-color-2-hue: 193.3;
--theme-nuance-color-2-saturation: 43.4%;
--theme-nuance-color-2-lightness: 67.5%;
--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
--theme-nuance-color-3-hue: 210.0;
--theme-nuance-color-3-saturation: 34.0%;
--theme-nuance-color-3-lightness: 63.1%;
--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
--theme-nuance-color-4-hue: 213.1;
--theme-nuance-color-4-saturation: 32.0%;
--theme-nuance-color-4-lightness: 52.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(32.5, 80%, 50%);
--theme-orange-color: hsl(32.5, 70%, 45%);
--theme-yellow-color: hsl(40.0, 0.6%, 73.3%);
--theme-green-color: hsl(92.4, 27.8%, 64.7%);
--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-orange-color);
--button-alert-border-hover: var(--theme-orange-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--secondary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(217.5,
calc(var(--secondary-color-1-saturation) + 35%),
calc(var(--secondary-color-1-lightness) - 30%));
--button-primary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 35%));
--button-primary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
--button-primary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
--button-secondary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
--button-secondary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
/* ---------active--------- */
--button-secondary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) + 40%),
calc(var(--theme-nuance-color-3-lightness) - 55%));
--button-secondary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-secondary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
/* ---------hover---------- */
--button-tertiary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
}
/*
.theme-template {
If light theme: should go from bright to darker
If dark theme: should go from dark to brighter
ideally this should not be anything but steps of
gray or slightly variants from it
--primary-color-1: #2E3440;
--primary-color-2: #3B4252;
--primary-color-3: #434C5E;
--primary-color-4: #4C566A;
If light theme: should go from dark to brighter
If dark theme: should go from bright to darker
ideally this should not be anything but steps of
gray or slightly variants from it
--secondary-color-1: #ECEFF4;
--secondary-color-2: #E5E9F0;
--secondary-color-3: #D8DEE9;
--secondary-color-4: #C8CED9;
Choose wisely nuance colors. It is not easy to find
4 harmonizing nuance colors. But keep in mind, that
only one accent color could work too.
--theme-nuance-color-1: #8FBCBB;
--theme-nuance-color-2: #88C0D0;
--theme-nuance-color-3: #81A1C1;
--theme-nuance-color-4: #5E81AC;
adapt the color red, orange, yellow, green,
purple to the 'mood' of your overall design
e.g is it low-contrast? vibrant? dynamic? etc
--theme-red-color: #BF616A;
--theme-orange-color: #D08770;
--theme-yellow-color: #EBCB8B;
--theme-green-color: #A3BE8C;
--theme-purple-color: #B48EAD;
NOTE: comment all those line `--- ...` out
------------------------------------------------
--background-color-1:
--background-color-2:
--background-color-3:
--background-color-4:
--border-color-1:
--border-color-2:
--border-color-3:
--border-focus-color:
--border-focus-shadow:
--text-color-plain:
--text-color-subtile-1:
--text-color-subtile-2:
--code-background-color:
--code-text-color:
--ui-range-thumb-color:
--ui-range-thumb-border:
--textarea-border-color:
-------------------------------------------
--button-alert-text-hover:
--button-alert-color-hover:
--button-alert-border-hover:
--button-alert-text-active:
--button-alert-color-active:
--button-alert-border-active:
----------- PRIMARY -----------------------
--button should immediately catch the eye--
--button-primary-text:
--button-primary-color:
--button-primary-border:
---------hover----------
--button-primary-text-hover:
--button-primary-color-hover:
--button-primary-border-hover:
---------active---------
--button-primary-text-active:
--button-primary-color-active:
--button-primary-border-active:
------------ SECONDARY ------------------------
--button should NOT immediately catch the eye--
--button-secondary-text:
--button-secondary-color:
--button-secondary-border:
---------hover----------
--button-secondary-text-hover:
--button-secondary-color-hover:
--button-secondary-border-hover:
---------active---------
--button-secondary-text-active:
--button-secondary-color-active:
--button-secondary-border-active:
---------- TERTIARY -----------------------
---------- disabled buttons ---------------
--button-tertiary-text:
--button-tertiary-color:
--button-tertiary-border:
---------hover----------
--button-tertiary-text:
--button-tertiary-color:
--button-tertiary-border:
}
*/

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const paramDefaults = {
stream: true,
n_predict: 500,
temperature: 0.2,
stop: ["</s>"]
};
let generation_settings = null;
// Completes the prompt as a generator. Recommended for most use cases.
//
// Example:
//
// import { llama } from '/completion.js'
//
// const request = llama("Tell me a joke", {n_predict: 800})
// for await (const chunk of request) {
// document.write(chunk.data.content)
// }
//
export async function* llama(prompt, params = {}, config = {}) {
let controller = config.controller;
const api_url = config.api_url?.replace(/\/+$/, '') || "";
if (!controller) {
controller = new AbortController();
}
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch(`${api_url}${config.endpoint || '/completion'}`, {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream',
...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {})
},
signal: controller.signal,
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let content = "";
let leftover = ""; // Buffer for partially read lines
try {
let cont = true;
while (cont) {
const result = await reader.read();
if (result.done) {
break;
}
// Add any leftover data to the current chunk of data
const text = leftover + decoder.decode(result.value);
// Check if the last character is a line break
const endsWithLineBreak = text.endsWith('\n');
// Split the text into lines
let lines = text.split('\n');
// If the text doesn't end with a line break, then the last line is incomplete
// Store it in leftover to be added to the next chunk of data
if (!endsWithLineBreak) {
leftover = lines.pop();
} else {
leftover = ""; // Reset leftover if we have a line break at the end
}
// Parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const line of lines) {
const match = regex.exec(line);
if (match) {
result[match[1]] = match[2];
if (result.data === '[DONE]') {
cont = false;
break;
}
// since we know this is llama.cpp, let's just decode the json in data
if (result.data) {
result.data = JSON.parse(result.data);
content += result.data.content;
// yield
yield result;
// if we got a stop token from server, we will break here
if (result.data.stop) {
if (result.data.generation_settings) {
generation_settings = result.data.generation_settings;
}
cont = false;
break;
}
}
if (result.error) {
try {
result.error = JSON.parse(result.error);
if (result.error.message.includes('slot unavailable')) {
// Throw an error to be caught by upstream callers
throw new Error('slot unavailable');
} else {
console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
}
} catch(e) {
console.error(`llama.cpp error ${result.error}`)
}
}
}
}
}
} catch (e) {
if (e.name !== 'AbortError') {
console.error("llama error: ", e);
}
throw e;
}
finally {
controller.abort();
}
return content;
}
// Call llama, return an event target that you can subscribe to
//
// Example:
//
// import { llamaEventTarget } from '/completion.js'
//
// const conn = llamaEventTarget(prompt)
// conn.addEventListener("message", (chunk) => {
// document.write(chunk.detail.content)
// })
//
export const llamaEventTarget = (prompt, params = {}, config = {}) => {
const eventTarget = new EventTarget();
(async () => {
let content = "";
for await (const chunk of llama(prompt, params, config)) {
if (chunk.data) {
content += chunk.data.content;
eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
}
if (chunk.data.generation_settings) {
eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
}
if (chunk.data.timings) {
eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
}
}
eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
})();
return eventTarget;
}
// Call llama, return a promise that resolves to the completed text. This does not support streaming
//
// Example:
//
// llamaPromise(prompt).then((content) => {
// document.write(content)
// })
//
// or
//
// const content = await llamaPromise(prompt)
// document.write(content)
//
export const llamaPromise = (prompt, params = {}, config = {}) => {
return new Promise(async (resolve, reject) => {
let content = "";
try {
for await (const chunk of llama(prompt, params, config)) {
content += chunk.data.content;
}
resolve(content);
} catch (error) {
reject(error);
}
});
};
/**
* (deprecated)
*/
export const llamaComplete = async (params, controller, callback) => {
for await (const chunk of llama(params.prompt, params, { controller })) {
callback(chunk);
}
}
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async (config = {}) => {
if (!generation_settings) {
const api_url = config.api_url?.replace(/\/+$/, '') || "";
const props = await fetch(`${api_url}/props`).then(r => r.json());
generation_settings = props.default_generation_settings;
}
return generation_settings;
}

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// WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first.
const SPACE_RULE = '| " " | "\\n"{1,2} [ \\t]{0,20}';
function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
if (maxItems == 0) {
return '';
}
if (minItems === 0 && maxItems === 1) {
return `${itemRule}?`;
}
const separatorRule = opts.separatorRule ?? '';
const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false
if (separatorRule === '') {
if (minItems === 1 && maxItems === undefined) {
return `${itemRule}+`;
} else if (minItems === 0 && maxItems === undefined) {
return `${itemRule}*`;
} else {
return `${itemRule}{${minItems},${maxItems !== undefined ? maxItems : ''}}`;
}
}
const result = itemRule + ' ' + _buildRepetition(`(${separatorRule} ${itemRule})`, minItems > 0 ? minItems - 1 : 0, maxItems !== undefined ? maxItems - 1 : undefined);
return minItems === 0 ? `(${result})?` : result;
}
function _generateMinMaxInt(minValue, maxValue, out, decimalsLeft = 16, topLevel = true) {
const hasMin = minValue !== null;
const hasMax = maxValue !== null;
function digitRange(fromChar, toChar) {
out.push("[");
if (fromChar === toChar) {
out.push(fromChar);
} else {
out.push(fromChar);
out.push("-");
out.push(toChar);
}
out.push("]");
}
function moreDigits(minDigits, maxDigits) {
out.push("[0-9]");
if (minDigits === maxDigits && minDigits === 1) {
return;
}
out.push("{");
out.push(minDigits.toString());
if (maxDigits !== minDigits) {
out.push(",");
if (maxDigits !== Number.MAX_SAFE_INTEGER) {
out.push(maxDigits.toString());
}
}
out.push("}");
}
function uniformRange(fromStr, toStr) {
let i = 0;
while (i < fromStr.length && fromStr[i] === toStr[i]) {
i++;
}
if (i > 0) {
out.push("\"");
out.push(fromStr.slice(0, i));
out.push("\"");
}
if (i < fromStr.length) {
if (i > 0) {
out.push(" ");
}
const subLen = fromStr.length - i - 1;
if (subLen > 0) {
const fromSub = fromStr.slice(i + 1);
const toSub = toStr.slice(i + 1);
const subZeros = "0".repeat(subLen);
const subNines = "9".repeat(subLen);
let toReached = false;
out.push("(");
if (fromSub === subZeros) {
digitRange(fromStr[i], String.fromCharCode(toStr.charCodeAt(i) - 1));
out.push(" ");
moreDigits(subLen, subLen);
} else {
out.push("[");
out.push(fromStr[i]);
out.push("] ");
out.push("(");
uniformRange(fromSub, subNines);
out.push(")");
if (fromStr.charCodeAt(i) < toStr.charCodeAt(i) - 1) {
out.push(" | ");
if (toSub === subNines) {
digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), toStr[i]);
toReached = true;
} else {
digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), String.fromCharCode(toStr.charCodeAt(i) - 1));
}
out.push(" ");
moreDigits(subLen, subLen);
}
}
if (!toReached) {
out.push(" | ");
digitRange(toStr[i], toStr[i]);
out.push(" ");
uniformRange(subZeros, toSub);
}
out.push(")");
} else {
out.push("[");
out.push(fromStr[i]);
out.push("-");
out.push(toStr[i]);
out.push("]");
}
}
}
if (hasMin && hasMax) {
if (minValue < 0 && maxValue < 0) {
out.push("\"-\" (");
_generateMinMaxInt(-maxValue, -minValue, out, decimalsLeft, true);
out.push(")");
return;
}
if (minValue < 0) {
out.push("\"-\" (");
_generateMinMaxInt(0, -minValue, out, decimalsLeft, true);
out.push(") | ");
minValue = 0;
}
let minS = minValue.toString();
const maxS = maxValue.toString();
const minDigits = minS.length;
const maxDigits = maxS.length;
for (let digits = minDigits; digits < maxDigits; digits++) {
uniformRange(minS, "9".repeat(digits));
minS = "1" + "0".repeat(digits);
out.push(" | ");
}
uniformRange(minS, maxS);
return;
}
const lessDecimals = Math.max(decimalsLeft - 1, 1);
if (hasMin) {
if (minValue < 0) {
out.push("\"-\" (");
_generateMinMaxInt(null, -minValue, out, decimalsLeft, false);
out.push(") | [0] | [1-9] ");
moreDigits(0, decimalsLeft - 1);
} else if (minValue === 0) {
if (topLevel) {
out.push("[0] | [1-9] ");
moreDigits(0, lessDecimals);
} else {
moreDigits(1, decimalsLeft);
}
} else if (minValue <= 9) {
const c = minValue.toString();
const range_start = topLevel ? '1' : '0';
if (c > range_start) {
digitRange(range_start, String.fromCharCode(c.charCodeAt(0) - 1));
out.push(" ");
moreDigits(1, lessDecimals);
out.push(" | ");
}
digitRange(c, "9");
out.push(" ");
moreDigits(0, lessDecimals);
} else {
const minS = minValue.toString();
const length = minS.length;
const c = minS[0];
if (c > "1") {
digitRange(topLevel ? "1" : "0", String.fromCharCode(c.charCodeAt(0) - 1));
out.push(" ");
moreDigits(length, lessDecimals);
out.push(" | ");
}
digitRange(c, c);
out.push(" (");
_generateMinMaxInt(parseInt(minS.slice(1)), null, out, lessDecimals, false);
out.push(")");
if (c < "9") {
out.push(" | ");
digitRange(String.fromCharCode(c.charCodeAt(0) + 1), "9");
out.push(" ");
moreDigits(length - 1, lessDecimals);
}
}
return;
}
if (hasMax) {
if (maxValue >= 0) {
if (topLevel) {
out.push("\"-\" [1-9] ");
moreDigits(0, lessDecimals);
out.push(" | ");
}
_generateMinMaxInt(0, maxValue, out, decimalsLeft, true);
} else {
out.push("\"-\" (");
_generateMinMaxInt(-maxValue, null, out, decimalsLeft, false);
out.push(")");
}
return;
}
throw new Error("At least one of minValue or maxValue must be set");
}
class BuiltinRule {
constructor(content, deps) {
this.content = content;
this.deps = deps || [];
}
}
const PRIMITIVE_RULES = {
boolean : new BuiltinRule('("true" | "false") space', []),
'decimal-part' : new BuiltinRule('[0-9]{1,16}', []),
'integral-part': new BuiltinRule('[0] | [1-9] [0-9]{0,15}', []),
number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']),
integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']),
value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']),
object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']),
array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']),
uuid : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []),
char : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []),
string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']),
null : new BuiltinRule('"null" space', []),
};
// TODO: support "uri", "email" string formats
const STRING_FORMAT_RULES = {
'date' : new BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []),
'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []),
'date-time' : new BuiltinRule('date "T" time', ['date', 'time']),
'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']),
'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']),
'date-time-string': new BuiltinRule('"\\"" date-time "\\"" space', ['date-time']),
}
const RESERVED_NAMES = {'root': true, ...PRIMITIVE_RULES, ...STRING_FORMAT_RULES};
const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g;
const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g;
const GRAMMAR_RANGE_LITERAL_ESCAPE_RE = /[\n\r"\]\-\\]/g;
const GRAMMAR_LITERAL_ESCAPES = { '\r': '\\r', '\n': '\\n', '"': '\\"', '-': '\\-', ']': '\\]' };
const NON_LITERAL_SET = new Set('|.()[]{}*+?');
const ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS = new Set('^$.[]()|{}*+?');
export class SchemaConverter {
constructor(options) {
this._propOrder = options.prop_order || {};
this._allowFetch = options.allow_fetch || false;
this._dotall = options.dotall || false;
this._rules = {'space': SPACE_RULE};
this._refs = {};
this._refsBeingResolved = new Set();
}
_formatLiteral(literal) {
const escaped = literal.replace(
GRAMMAR_LITERAL_ESCAPE_RE,
m => GRAMMAR_LITERAL_ESCAPES[m]
);
return `"${escaped}"`;
}
_formatRangeChar(literal) {
return JSON.stringify(literal).slice(1, -1).replace(
GRAMMAR_RANGE_LITERAL_ESCAPE_RE,
m => GRAMMAR_LITERAL_ESCAPES[m]
);
}
_addRule(name, rule) {
let escName = name.replace(INVALID_RULE_CHARS_RE, '-');
let key = escName;
if (escName in this._rules) {
if (this._rules[escName] === rule) {
return key;
}
let i = 0;
while ((`${escName}${i}` in this._rules) && (this._rules[`${escName}${i}`] !== rule)) {
i += 1;
}
key = `${escName}${i}`;
}
this._rules[key] = rule;
return key;
}
async resolveRefs(schema, url) {
const visit = async (n) => {
if (Array.isArray(n)) {
return Promise.all(n.map(visit));
} else if (typeof n === 'object' && n !== null) {
let ref = n.$ref;
let target;
if (ref !== undefined && !this._refs[ref]) {
if (ref.startsWith('https://')) {
if (!this._allowFetch) {
throw new Error('Fetching remote schemas is not allowed (use --allow-fetch for force)');
}
const fetch = (await import('node-fetch')).default;
const fragSplit = ref.split('#');
const baseUrl = fragSplit[0];
target = this._refs[baseUrl];
if (!target) {
target = await this.resolveRefs(await fetch(ref).then(res => res.json()), baseUrl);
this._refs[baseUrl] = target;
}
if (fragSplit.length === 1 || fragSplit[fragSplit.length - 1] === '') {
return target;
}
} else if (ref.startsWith('#/')) {
target = schema;
ref = `${url}${ref}`;
n.$ref = ref;
} else {
throw new Error(`Unsupported ref ${ref}`);
}
const selectors = ref.split('#')[1].split('/').slice(1);
for (const sel of selectors) {
if (!target || !(sel in target)) {
throw new Error(`Error resolving ref ${ref}: ${sel} not in ${JSON.stringify(target)}`);
}
target = target[sel];
}
this._refs[ref] = target;
} else {
await Promise.all(Object.values(n).map(visit));
}
}
return n;
};
return visit(schema);
}
_generateUnionRule(name, altSchemas) {
return altSchemas
.map((altSchema, i) => this.visit(altSchema, `${name ?? ''}${name ? '-' : 'alternative-'}${i}`))
.join(' | ');
}
_visitPattern(pattern, name) {
if (!pattern.startsWith('^') || !pattern.endsWith('$')) {
throw new Error('Pattern must start with "^" and end with "$"');
}
pattern = pattern.slice(1, -1);
const subRuleIds = {};
let i = 0;
const length = pattern.length;
const getDot = () => {
let rule;
if (this._dotall) {
rule = '[\\U00000000-\\U0010FFFF]';
} else {
// Accept any character... except \n and \r line break chars (\x0A and \xOD)
rule = '[^\\x0A\\x0D]';
}
return this._addRule('dot', rule);
};
const toRule = ([s, isLiteral]) => isLiteral ? "\"" + s + "\"" : s;
const transform = () => {
const start = i;
// For each component of this sequence, store its string representation and whether it's a literal.
// We only need a flat structure here to apply repetition operators to the last item, and
// to merge literals at the and (we're parsing grouped ( sequences ) recursively and don't treat '|' specially
// (GBNF's syntax is luckily very close to regular expressions!)
const seq = [];
const joinSeq = () => {
const ret = [];
for (const [isLiteral, g] of groupBy(seq, x => x[1])) {
if (isLiteral) {
ret.push([[...g].map(x => x[0]).join(''), true]);
} else {
ret.push(...g);
}
}
if (ret.length === 1) {
return ret[0];
}
return [ret.map(x => toRule(x)).join(' '), false];
};
while (i < length) {
const c = pattern[i];
if (c === '.') {
seq.push([getDot(), false]);
i += 1;
} else if (c === '(') {
i += 1;
if (i < length) {
if (pattern[i] === '?') {
throw new Error(`Unsupported pattern syntax "${pattern[i]}" at index ${i} of /${pattern}/`);
}
}
seq.push([`(${toRule(transform())})`, false]);
} else if (c === ')') {
i += 1;
if (start <= 0 || pattern[start - 1] !== '(') {
throw new Error(`Unbalanced parentheses; start = ${start}, i = ${i}, pattern = ${pattern}`);
}
return joinSeq();
} else if (c === '[') {
let squareBrackets = c;
i += 1;
while (i < length && pattern[i] !== ']') {
if (pattern[i] === '\\') {
squareBrackets += pattern.slice(i, i + 2);
i += 2;
} else {
squareBrackets += pattern[i];
i += 1;
}
}
if (i >= length) {
throw new Error(`Unbalanced square brackets; start = ${start}, i = ${i}, pattern = ${pattern}`);
}
squareBrackets += ']';
i += 1;
seq.push([squareBrackets, false]);
} else if (c === '|') {
seq.push(['|', false]);
i += 1;
} else if (c === '*' || c === '+' || c === '?') {
seq[seq.length - 1] = [toRule(seq[seq.length - 1]) + c, false];
i += 1;
} else if (c === '{') {
let curlyBrackets = c;
i += 1;
while (i < length && pattern[i] !== '}') {
curlyBrackets += pattern[i];
i += 1;
}
if (i >= length) {
throw new Error(`Unbalanced curly brackets; start = ${start}, i = ${i}, pattern = ${pattern}`);
}
curlyBrackets += '}';
i += 1;
const nums = curlyBrackets.slice(1, -1).split(',').map(s => s.trim());
let minTimes, maxTimes;
if (nums.length === 1) {
minTimes = parseInt(nums[0], 10);
maxTimes = minTimes;
} else {
if (nums.length !== 2) {
throw new Error(`Invalid quantifier ${curlyBrackets}`);
}
minTimes = nums[0] ? parseInt(nums[0], 10) : 0;
maxTimes = nums[1] ? parseInt(nums[1], 10) : Infinity;
}
let [sub, subIsLiteral] = seq[seq.length - 1];
if (!subIsLiteral) {
let id = subRuleIds[sub];
if (id === undefined) {
id = this._addRule(`${name}-${Object.keys(subRuleIds).length + 1}`, sub);
subRuleIds[sub] = id;
}
sub = id;
}
seq[seq.length - 1] = [
_buildRepetition(subIsLiteral ? `"${sub}"` : sub, minTimes, maxTimes, {itemRuleIsLiteral: subIsLiteral}),
false
];
} else {
let literal = '';
while (i < length) {
if (pattern[i] === '\\' && i < length - 1) {
const next = pattern[i + 1];
if (ESCAPED_IN_REGEXPS_BUT_NOT_IN_LITERALS.has(next)) {
i += 1;
literal += pattern[i];
i += 1;
} else {
literal += pattern.slice(i, i + 2);
i += 2;
}
} else if (pattern[i] === '"') {
literal += '\\"';
i += 1;
} else if (!NON_LITERAL_SET.has(pattern[i]) &&
(i === length - 1 || literal === '' || pattern[i + 1] === '.' || !NON_LITERAL_SET.has(pattern[i+1]))) {
literal += pattern[i];
i += 1;
} else {
break;
}
}
if (literal !== '') {
seq.push([literal, true]);
}
}
}
return joinSeq();
};
return this._addRule(name, "\"\\\"\" (" + toRule(transform()) + ") \"\\\"\" space")
}
_notStrings(strings) {
class TrieNode {
constructor() {
this.children = {};
this.isEndOfString = false;
}
insert(str) {
let node = this;
for (const c of str) {
node = node.children[c] = node.children[c] || new TrieNode();
}
node.isEndOfString = true;
}
}
const trie = new TrieNode();
for (const s of strings) {
trie.insert(s);
}
const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
const out = ['["] ( '];
const visit = (node) => {
const rejects = [];
let first = true;
for (const c of Object.keys(node.children).sort()) {
const child = node.children[c];
rejects.push(c);
if (first) {
first = false;
} else {
out.push(' | ');
}
out.push(`[${c}]`);
if (Object.keys(child.children).length > 0) {
out.push(' (');
visit(child);
out.push(')');
} else if (child.isEndOfString) {
out.push(` ${charRuleName}+`);
}
}
if (Object.keys(node.children).length > 0) {
if (!first) {
out.push(' | ');
}
out.push(`[^"${rejects.join('')}] ${charRuleName}*`);
}
};
visit(trie);
out.push(` )${trie.isEndOfString ? '' : '?'} ["] space`);
return out.join('');
}
_resolveRef(ref) {
let refName = ref.split('/').pop();
if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) {
this._refsBeingResolved.add(ref);
const resolved = this._refs[ref];
refName = this.visit(resolved, refName);
this._refsBeingResolved.delete(ref);
}
return refName;
}
_generateConstantRule(value) {
return this._formatLiteral(JSON.stringify(value));
}
visit(schema, name) {
const schemaType = schema.type;
const schemaFormat = schema.format;
const ruleName = name in RESERVED_NAMES ? name + '-' : name == '' ? 'root' : name;
const ref = schema.$ref;
if (ref !== undefined) {
return this._addRule(ruleName, this._resolveRef(ref));
} else if (schema.oneOf || schema.anyOf) {
return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf));
} else if (Array.isArray(schemaType)) {
return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({...schema, type: t}))));
} else if ('const' in schema) {
return this._addRule(ruleName, this._generateConstantRule(schema.const) + ' space');
} else if ('enum' in schema) {
const rule = '(' + schema.enum.map(v => this._generateConstantRule(v)).join(' | ') + ') space';
return this._addRule(ruleName, rule);
} else if ((schemaType === undefined || schemaType === 'object') &&
('properties' in schema ||
('additionalProperties' in schema && schema.additionalProperties !== true))) {
const required = new Set(schema.required || []);
const properties = Object.entries(schema.properties ?? {});
return this._addRule(ruleName, this._buildObjectRule(properties, required, name, schema.additionalProperties));
} else if ((schemaType === undefined || schemaType === 'object') && 'allOf' in schema) {
const required = new Set();
const properties = [];
const addComponent = (compSchema, isRequired) => {
const ref = compSchema.$ref;
if (ref !== undefined) {
compSchema = this._refs[ref];
}
if ('properties' in compSchema) {
for (const [propName, propSchema] of Object.entries(compSchema.properties)) {
properties.push([propName, propSchema]);
if (isRequired) {
required.add(propName);
}
}
}
};
for (const t of schema.allOf) {
if ('anyOf' in t) {
for (const tt of t.anyOf) {
addComponent(tt, false);
}
} else {
addComponent(t, true);
}
}
return this._addRule(ruleName, this._buildObjectRule(properties, required, name, null));
} else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) {
const items = schema.items ?? schema.prefixItems;
if (Array.isArray(items)) {
return this._addRule(
ruleName,
'"[" space ' +
items.map((item, i) => this.visit(item, `${name ?? ''}${name ? '-' : ''}tuple-${i}`)).join(' "," space ') +
' "]" space'
);
} else {
const itemRuleName = this.visit(items, `${name ?? ''}${name ? '-' : ''}item`);
const minItems = schema.minItems || 0;
const maxItems = schema.maxItems;
return this._addRule(ruleName, '"[" space ' + _buildRepetition(itemRuleName, minItems, maxItems, {separatorRule: '"," space'}) + ' "]" space');
}
} else if ((schemaType === undefined || schemaType === 'string') && 'pattern' in schema) {
return this._visitPattern(schema.pattern, ruleName);
} else if ((schemaType === undefined || schemaType === 'string') && /^uuid[1-5]?$/.test(schema.format || '')) {
return this._addPrimitive(
ruleName === 'root' ? 'root' : schemaFormat,
PRIMITIVE_RULES['uuid']
);
} else if ((schemaType === undefined || schemaType === 'string') && `${schema.format}-string` in STRING_FORMAT_RULES) {
const primName = `${schema.format}-string`
return this._addRule(ruleName, this._addPrimitive(primName, STRING_FORMAT_RULES[primName]));
} else if (schemaType === 'string' && ('minLength' in schema || 'maxLength' in schema)) {
const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
const minLen = schema.minLength || 0;
const maxLen = schema.maxLength;
return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space');
} else if (schemaType === 'integer' && ('minimum' in schema || 'exclusiveMinimum' in schema || 'maximum' in schema || 'exclusiveMaximum' in schema)) {
let minValue = null;
let maxValue = null;
if ('minimum' in schema) {
minValue = schema.minimum;
} else if ('exclusiveMinimum' in schema) {
minValue = schema.exclusiveMinimum + 1;
}
if ('maximum' in schema) {
maxValue = schema.maximum;
} else if ('exclusiveMaximum' in schema) {
maxValue = schema.exclusiveMaximum - 1;
}
const out = ["("];
_generateMinMaxInt(minValue, maxValue, out);
out.push(") space");
return this._addRule(ruleName, out.join(''));
} else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object']));
} else {
if (!(schemaType in PRIMITIVE_RULES)) {
throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`);
}
// TODO: support minimum, maximum, exclusiveMinimum, exclusiveMaximum at least for zero
return this._addPrimitive(ruleName === 'root' ? 'root' : schemaType, PRIMITIVE_RULES[schemaType]);
}
}
_addPrimitive(name, rule) {
let n = this._addRule(name, rule.content);
for (const dep of rule.deps) {
const depRule = PRIMITIVE_RULES[dep] || STRING_FORMAT_RULES[dep];
if (!depRule) {
throw new Error(`Rule ${dep} not known`);
}
if (!(dep in this._rules)) {
this._addPrimitive(dep, depRule);
}
}
return n;
}
_buildObjectRule(properties, required, name, additionalProperties) {
const propOrder = this._propOrder;
// sort by position in prop_order (if specified) then by original order
const sortedProps = properties.map(([k]) => k).sort((a, b) => {
const orderA = propOrder[a] || Infinity;
const orderB = propOrder[b] || Infinity;
return orderA - orderB || properties.findIndex(([k]) => k === a) - properties.findIndex(([k]) => k === b);
});
const propKvRuleNames = {};
for (const [propName, propSchema] of properties) {
const propRuleName = this.visit(propSchema, `${name ?? ''}${name ? '-' : ''}${propName}`);
propKvRuleNames[propName] = this._addRule(
`${name ?? ''}${name ? '-' : ''}${propName}-kv`,
`${this._formatLiteral(JSON.stringify(propName))} space ":" space ${propRuleName}`
);
}
const requiredProps = sortedProps.filter(k => required.has(k));
const optionalProps = sortedProps.filter(k => !required.has(k));
if (additionalProperties) {
const subName = `${name ?? ''}${name ? '-' : ''}additional`;
const valueRule =
additionalProperties != null && typeof additionalProperties === 'object' ? this.visit(additionalProperties, `${subName}-value`)
: this._addPrimitive('value', PRIMITIVE_RULES['value']);
const key_rule =
sortedProps.length === 0 ? this._addPrimitive('string', PRIMITIVE_RULES['string'])
: this._addRule(`${subName}-k`, this._notStrings(sortedProps));
propKvRuleNames['*'] = this._addRule(
`${subName}-kv`,
`${key_rule} ":" space ${valueRule}`);
optionalProps.push('*');
}
let rule = '"{" space ';
rule += requiredProps.map(k => propKvRuleNames[k]).join(' "," space ');
if (optionalProps.length > 0) {
rule += ' (';
if (requiredProps.length > 0) {
rule += ' "," space ( ';
}
const getRecursiveRefs = (ks, firstIsOptional) => {
const [k, ...rest] = ks;
const kvRuleName = propKvRuleNames[k];
let res;
const commaRef = `( "," space ${kvRuleName} )`;
if (firstIsOptional) {
res = commaRef + (k === '*' ? '*' : '?');
} else {
res = kvRuleName + (k === '*' ? ' ' + commaRef + '*' : '');
}
if (rest.length > 0) {
res += ' ' + this._addRule(
`${name ?? ''}${name ? '-' : ''}${k}-rest`,
getRecursiveRefs(rest, true)
);
}
return res;
};
rule += optionalProps.map((_, i) => getRecursiveRefs(optionalProps.slice(i), false)).join(' | ');
if (requiredProps.length > 0) {
rule += ' )';
}
rule += ' )?';
}
rule += ' "}" space';
return rule;
}
formatGrammar() {
let grammar = '';
for (const [name, rule] of Object.entries(this._rules).sort(([a], [b]) => a.localeCompare(b))) {
grammar += `${name} ::= ${rule}\n`;
}
return grammar;
}
}
// Helper function to group elements by a key function
function* groupBy(iterable, keyFn) {
let lastKey = null;
let group = [];
for (const element of iterable) {
const key = keyFn(element);
if (lastKey !== null && key !== lastKey) {
yield [lastKey, group];
group = [];
}
group.push(element);
lastKey = key;
}
if (group.length > 0) {
yield [lastKey, group];
}
}

12
tools/server/public_legacy/loading.html Normal file
View file

@ -0,0 +1,12 @@
<!DOCTYPE html>
<html>
<head>
<meta http-equiv="refresh" content="5">
</head>
<body>
<div id="loading">
The model is loading. Please wait.<br/>
The user interface will appear soon.
</div>
</body>
</html>

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tools/server/public_legacy/prompt-formats.js Normal file
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// extended list
export const promptFormats = {
"alpaca": {
template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}`,
char: "Response",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Instruction",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"chatml": {
template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`,
historyTemplate: `<|im_start|>{{name}}\n{{message}}`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "<|im_end|>\n",
stops: ""
},
// ----------------------------
"commandr": {
template: `<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`,
historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`,
char: "CHATBOT_TOKEN",
charMsgPrefix: "",
charMsgSuffix: "",
user: "USER_TOKEN",
userMsgPrefix: "",
userMsgSuffix: "<|END_OF_TURN_TOKEN|>",
stops: ""
},
// ref: https://docs.cohere.com/docs/prompting-command-r
// ----------------------------
"llama2": {
template: `<s>[INST] <<SYS>>\n{{prompt}}\n<</SYS>>\n\nTest Message [/INST] Test Successfull </s>{{history}}{{char}}`,
historyTemplate: `{{name}}: {{message}}`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "</s>",
user: "User",
userMsgPrefix: "<s>[INST] ",
userMsgSuffix: " [/INST]",
stops: ""
},
// ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2
// ----------------------------
"llama3": {
template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`,
historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "",
stops: "<|eot_id|>"
},
// ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3
// ----------------------------
"openchat": {
template: `{{history}}{{char}}`,
historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"phi3": {
template: `{{history}}{{char}}`,
historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "",
stops: "<|end|>"
},
// ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format
// ----------------------------
"vicuna": {
template: `{{prompt}}\n{{history}}{{char}}`,
historyTemplate: `{{name}}: {{message}}\n`,
char: "ASSISTANT",
charMsgPrefix: "",
charMsgSuffix: "",
user: "USER",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1
// ----------------------------
"deepseekCoder": {
template: `{{prompt}}{{history}}{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}`,
char: "Response",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Instruction",
userMsgPrefix: "",
userMsgSuffix: "",
stops: "<|EOT|>"
},
// ----------------------------
"med42": {
template: `<|system|>: {{prompt}}\n{{history}}{{char}}`,
historyTemplate: `<|{{name}}|>: {{message}}\n`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "prompter",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"neuralchat": {
template: `### System:\n{{prompt}}\n{{history}}{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}\n`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"nousHermes": {
template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`,
historyTemplate: `### {{name}}:\n{{message}}`,
char: "Response",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Input",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"openchatMath": {
template: `{{history}}{{char}}`,
historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"orion": {
template: `<s>Human: Test Message\n\nAssistant: </s>Test Successful</s>{{history}}{{char}}:`,
historyTemplate: `{{name}}: {{message}}`,
char: "Assistant </s>",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Human",
userMsgPrefix: "",
userMsgSuffix: "\n\n",
stops: ""
},
// ----------------------------
"sauerkraut": {
template: `{{prompt}}\n{{history}}{{char}}`,
historyTemplate: `
{{name}}: {{message}}\n`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"starlingCode": {
template: `{{history}}{{char}}`,
historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "User",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"yi34b": {
template: `{{history}} {{char}}`,
historyTemplate: `{{name}}: {{message}}`,
char: "Assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "Human",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
},
// ----------------------------
"zephyr": {
template: `<|system|>\n{{prompt}}</s>\n{{history}}{{char}}`,
historyTemplate: `<|{{name}}|>\n{{message}}</s>\n`,
char: "assistant",
charMsgPrefix: "",
charMsgSuffix: "",
user: "user",
userMsgPrefix: "",
userMsgSuffix: "",
stops: ""
}
};

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@import url("colorthemes.css");
body {
font-family: 'Arial', sans-serif;
font-size: 90%;
background-color: var(--background-color-1);
color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */
max-width: 600px;
min-width: 300px;
line-height: 1.2;
margin: 0 auto;
padding: 0 0.5em;
transition: background-color 0.3s;
}
::selection {
color: var(--button-primary-text) ;
background: var(--button-primary-color);
}
code, pre code {
font-family: 'Courier New', monospace;
}
#container {
margin: 0em auto;
display: flex;
flex-direction: column;
justify-content: space-between;
height: 100%;
}
main {
margin: 3px;
display: flex;
flex-direction: column;
justify-content: space-between;
gap: 1em;
flex-grow: 1;
overflow-y: auto;
border: 1px solid var(--border-color-3);
border-radius: 5px;
padding: 0.5em;
}
p {
overflow-wrap: break-word;
word-wrap: break-word;
hyphens: auto;
margin-top: 0.5em;
margin-bottom: 0.5em;
}
#write form {
margin: 1em 0 0 0;
display: flex;
flex-direction: column;
gap: 0.5em;
align-items: stretch;
}
.right {
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
margin-bottom: 30px;
}
.two-columns {
width: 97%;
max-width: 97%;
display: grid;
grid-template-columns: 1fr 1fr;
gap: 1em;
position: relative;
}
.json-schema-controls {
margin-top: 10px;
width: 100%;
max-width: 100%;
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
.json-schema-controls > * {
flex: 1;
}
/* titles of the details-summary boxes */
.summary-title {
font-weight: 600;
font-size: x-small;
color: var(--text-color-subtile-1);
text-transform: uppercase;
/* transition: ; */
}
fieldset {
border: none;
padding: 0;
margin: 0;
color: var(--text-color-plain);
}
fieldset.two {
display: grid;
grid-template: "a a a";
gap: 1em;
align-items: center;
font-size: x-small;
color: var(--text-color-plain);
}
fieldset.three {
display: grid;
grid-template: "a a a";
gap: 1em;
font-size: x-small;
color: var(--text-color-plain);
}
/* titles of name fields*/
fieldset.names {
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
/* titles of params fields*/
fieldset.params {
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-4);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
fieldset.dropdowns {
-webkit-appearance: none;
display: flex;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: red;
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
/* input of name fields*/
.names input[type="text"] {
font-family: Arial, sans-serif;
font-size: medium;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
}
.chat-id-color {
color: var(--chat-id-color);
}
details {
border: 1px solid var(--border-color-2);
border-radius: 5px;
padding: 0.5em 0.5em 0;
margin-top: 0.5em;
}
summary {
font-weight: bold;
margin: -0.5em -0.5em 0;
padding: 0.5em;
cursor: pointer;
}
details[open] {
padding: 0.5em;
}
textarea-sec, input-sec, button-sec {
padding: 10px;
height: 40px;
align-items: center;
}
textarea-sec::placeholder, input-sec::placeholder {
padding-left: 10px;
}
.toggleCheckbox {
display: none;
}
.toggleContainer {
position: relative;
display: grid;
grid-template-columns: repeat(2, 1fr);
width: fit-content;
border: 3px solid var(--border-color-2);
border-radius: 20px;
background: var(--border-color-2);
font-size: small;
cursor: pointer;
overflow: hidden;
}
/* toggle button current state */
.toggleContainer::before {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
content: '';
position: absolute;
width: 50%;
height: 100%;
left: 0%;
border-radius: 20px;
transition: all 0.3s;
}
.toggleContainer div {
padding: 6px;
text-align: center;
z-index: 1;
transition: color 0.3s;
}
.toggleCheckbox:checked + .toggleContainer::before {
left: 50%;
}
.toggleCheckbox:checked + .toggleContainer div:first-child {
color: var(--text-color-subtile-2);
}
.toggleCheckbox:checked + .toggleContainer div:last-child {
color: var(--button-primary-text);
}
.toggleCheckbox + .toggleContainer div:first-child {
color: var(--button-primary-text);
}
.toggleCheckbox + .toggleContainer div:last-child {
color: var(--text-color-subtile-2);
}
select {
padding: 5px;
margin-right: 5px;
border-radius: 4px;
border: 1px solid var(--secondary-color-4);
background-color: var(--primary-color-3);
color: var(--secondary-color-4);
cursor: pointer;
}
select:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 1px var(--border-focus-shadow);
}
.button-container {
display: flex;
justify-content: flex-end;
}
button {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
border: 1px solid var(--button-primary-border);
transition: background-color 0.1s;
border-radius: 12px;
font-size: x-small;
font-weight: 600;
text-shadow: 0px 0px 30px #ffffff;
text-align: center;
text-decoration: none;
margin: 4px 2px;
padding: 10px 20px;
display: inline-block;
cursor: pointer;
}
button:hover {
color: var(--button-primary-text-hover);
background-color: var(--button-primary-color-hover);
border: 1px solid var(--button-primary-border-hover);
font-size: x-small;
font-weight: 600;
}
button:active {
color: var(--button-primary-text-active);
background-color: var(--button-primary-color-active);
border: 1px solid var(--button-primary-border-active);
font-size: x-small;
font-weight: 600;
}
button:disabled {
color: var(--button-tertiary-text);
background-color: var(--button-tertiary-color);
border: 1px solid var(--button-tertiary-border);
font-size: x-small;
font-weight: 600;
cursor: not-allowed;
}
.reset-button {
background-color: var(--button-secondary-color);
border: 1px solid var(--button-secondary-color);
color: var(--button-secondary-text);
width: fit-content;
height: fit-content;
font-size: x-small;
font-weight: 600;
border-radius: 50px;
overflow: hidden;
}
.reset-button:hover {
color: var(--button-alert-text-hover);
background-color: var(--button-alert-color-hover);
border: 1px solid var(--button-alert-border-hover);
font-size: x-small;
font-weight: 600;
}
.reset-button:active {
color: var(--button-alert-text-active);
background-color: var(--button-alert-color-active);
border: 1px solid var(--button-alert-border-active);
font-size: x-small;
font-weight: 600;
}
.button-grammar {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
border: 1px solid var(--button-primary-border);
border-radius: 10px;
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: x-small;
font-weight: 600;
margin: 2px 2px;
transition: background-color 0.1s;
cursor: pointer;
}
.button-grammar:hover {
color: var(--button-primary-text-hover);
background-color: var(--button-primary-color-hover);
border: 1px solid var(--button-primary-border-hover);
border-radius: 10px;
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: x-small;
font-weight: 600;
margin: 2px 2px;
transition: background-color 0.1s;
cursor: pointer;
}
.button-grammar:active {
color: var(--button-primary-text-active);
background-color: var(--button-primary-color-active);
border: 1px solid var(--button-primary-border-active);
font-size: x-small;
font-weight: 600;
}
.button-back {
background-color: var(--button-secondary-color);
border: 1px solid var(--button-secondary-color);
color: var(--button-secondary-text);
transition: background-color 0.1s;
border-radius: 12px;
font-size: x-small;
font-weight: 600;
text-align: center;
text-decoration: none;
margin: 4px 2px;
padding: 10px 20px;
display: inline-block;
cursor: pointer;
}
.button-back:hover {
color: var(--button-secondary-text-hover);
background-color: var(--button-secondary-color-hover);
border: 1px solid var(--button-secondary-border-hover);
padding: 10px 20px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: x-small;
font-weight: 600;
margin: 4px 2px;
transition: background-color 0.1s;
cursor: pointer;
border-radius: 12px;
}
.button-back:active {
color: var(--button-secondary-text-active);
background-color: var(--button-secondary-color-active);
border: 1px solid var(--button-secondary-border-active);
font-size: x-small;
font-weight: 600;
}
.prob-set {
padding: 0.3em;
border-bottom: 1px solid red; /* unknown */
}
.popover-content {
position: absolute;
background-color: white;
padding: 0.2em;
box-shadow: 0 0 13px rgba(0, 0, 0, 0.1);
}
.grammar {
width: 97%;
max-width: 97%;
}
textarea {
padding: 5px;
flex-grow: 1;
width: 100%;
max-width: 100%;
border-radius: 8px;
border: 1px solid var(--border-color-1);
resize: none;
height: 6em;
}
textarea:focus {
outline: none;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* "props" frame */
input[type="text"],
input[type="range"] {
padding: 5px;
border-radius: 8px;
border: 1px solid var(--border-color-1);
}
/* "names and props" frame focused*/
input[type="text"]:focus {
outline: none;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
input[type="range"]:hover {
opacity: 1;
}
input[type="range"]:focus {
outline: none;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
background-size: var(--slider-track-size-focus);
}
input[type="range"]::-moz-range-thumb {
width: 6px;
height: 25px;
border: 1px solid var(--ui-range-thumb-border);
border-radius: 5px;
background-color: var(--ui-range-thumb-color);
cursor: pointer;
}
input[type="range"] {
-webkit-appearance: none;
width: 80%;
height: 1px;
border: 1px solid var(--border-color-1);
border-radius: 8px;
background: var(--border-color-2);
outline: none;
opacity: 0.7;
-webkit-transition: .2s;
transition: opacity .2s;
}
input[type="range"]::-webkit-slider-thumb {
-webkit-appearance: none;
appearance: none;
width: 6px;
height: 25px;
border: 1px solid var(--ui-range-thumb-border);
border-radius: 5px;
background-color: var(--ui-range-thumb-color);
cursor: pointer;
}
input[type="range"]::-webkit-slider-runnable-track {
background-size: var(--slider-track-size);
}
input[type="radio"] {
accent-color: var(--theme-nuance-color-2);
}
.chat-input-container {
position: relative;
max-width: 97%;
min-width: 97%;
}
.chat-input-label {
position: absolute;
top: 0;
left: 0;
color: var(--text-color-plain);
pointer-events: none;
margin-left: 5px;
margin-top: 5px;
}
textarea#chat-input {
padding-top: 10px;
padding-left: 10px;
font-size: medium;
border: 1px solid var(--border-color-2);
resize: vertical;
}
textarea#chat-input:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
.input-container {
position: relative;
box-sizing: border-box;
width: 100%; /* Setzt die Breite auf 100% */
max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */
}
.input-container:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* titles of name fields*/
/* fieldset.names {
display: grid;
grid-template: "a a";
gap: 1em;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
} */
/* input of name fields*/
/* .names input[type="text"] {
font-family: Arial, sans-serif;
font-size: medium;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
} */
fieldset.apiKey {
width: 100%;
font-size: x-small;
color: var(--theme-nuance-color-3);
padding-top: 16px;
padding-bottom: 16px;
text-transform: uppercase;
font-weight: 600;
}
.apiKey {
font-family: Arial, sans-serif;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
}
.apiKey:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
.apiKey input[type="text"] {
font-family: Arial, sans-serif;
font-size: medium;
font-weight: 500;
padding: 5px;
border: 1px solid var(--border-color-2);
}
.apiKey label {
display: inline-block;
width: auto;
margin-right: 5px;
}
textarea#api_key {
padding-top: 10px;
padding-left: 10px;
font-size: medium;
border: 1px solid var(--border-color-2);
resize: vertical;
}
textarea#api_key:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* embedded title of the system prompt text area */
.input-label {
position: absolute;
top: 0;
left: 0;
color: var(--theme-nuance-color-4);
pointer-events: none;
border-radius: 8px 8px 0px 0px;
padding-top: 10px;
padding-left: 13px;
padding-right: 0px;
margin-top: 1px;
margin-left: 1px;
margin-right: 20px;
text-transform: uppercase;
font-weight: 600;
font-size: small;
background: rgba(255, 255, 255, 0.5);
backdrop-filter: blur(10px);
-webkit-backdrop-filter: blur(10px); /* for safari */
width: 97%;
/* display: block;
box-sizing: border-box; */
}
/* embedded title of the prompt style areas */
.input-label-sec {
position: absolute;
top: 0;
left: 0;
color: var(--theme-nuance-color-4);
pointer-events: none;
margin-left: 13px;
margin-top: 16px;
text-transform: uppercase;
font-weight: 600;
font-size: x-small;
}
/* system prompt input area */
textarea.persistent-input {
padding-top: 42px;
padding-left: 11px;
width: 97%;
max-width: 97%;
height: 50px;
font-size: medium;
overscroll-behavior: contain;
}
/* system prompt box */
.persistent-input {
height: auto;
width: 100%;
max-width: 100%;
min-height: 50px;
padding: 3px;
transition: min-height 0.3s ease;
}
/* chat history box */
.persistent-input:focus {
height: auto;
min-height: 150px;
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
textarea.persistent-input:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* prompt style input area */
textarea.persistent-input-sec {
width: 97%;
max-width: 97%;
padding-top: 42px;
padding-left: 11px;
font-size: small;
border: 1px solid var(--border-color-1);
overscroll-behavior: contain;
}
textarea.persistent-input-sec:focus {
border: 1px solid var(--border-focus-color);
box-shadow: 0 0 3px var(--border-focus-shadow);
}
/* chat history box */
.persistent-input-sec {
height: auto;
min-height: 150px;
}
img {
border-radius: 8px;
display: block;
margin-left: auto;
margin-right: auto;
width: 50%;
}
/* code area background */
pre code {
display: block;
background-color: var(--code-background-color);
color: var(--code-text-color);
padding: 0.2em 0.2em;
border-radius: 5px;
}
/* code area text */
code {
font-family: monospace;
font-weight: bold;
padding: 0.1em 0.3em;
border-radius: 5px;
}
fieldset label {
margin: 0.5em 0;
display: block;
}
fieldset label.slim {
margin: 0 0.5em;
display: inline;
}
header {
display: flex;
justify-content: space-between;
align-items: center;
text-align: center;
padding-left: 15px;
}
.generation-statistics:hover {
color: var(--theme-nuance-color-4);
cursor: default;
}
footer {
font-size: 80%;
color: var(--background-color-3);
text-align: center;
cursor: default;
}
footer a {
color: var(--background-color-4); /* Color of the link */
text-decoration: none; /* No underlining */
font-weight: bold; /* Bold print */
}
footer a:hover {
color: var(--theme-nuance-color-4); /* Color of the link when hovering */
text-decoration: underline; /* Underlining when hovering */
}
.mode-chat textarea[name=prompt] {
height: 8.5em;
border: 1px solid var(--primary-color-3);
}
.mode-completion textarea[name=prompt] {
height: 30em;
border: 1px solid var(--primary-color-3);
}
@keyframes loading-bg-wipe {
0% {
background-position: 0%;
}
100% {
background-position: 100%;
}
}
.loading {
background-size: 50% 100%;
background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1));
animation: loading-bg-wipe 2s linear infinite;
}
.dropbtn {
color: var(--button-primary-color);
background-color: var(--background-color-1);
border: 1px solid var(--background-color-1);
transition: background-color 0.1s;
border-radius: 4px 4px 0px 0px;
font-size: x-small;
font-weight: 600;
text-shadow: 0px 0px 2px #99999990;
text-align: center;
text-decoration: none;
margin: 4px 2px;
padding: 5px 20px;
display: inline-block;
cursor: pointer;
top: 0;
}
.dropbtn svg {
vertical-align: middle;
margin-right: 0px;
stroke: var(--button-primary-color);
}
.dropbtn:hover svg {
vertical-align: middle;
margin-right: 0px;
stroke: var(--button-primary-text);
}
.dropbtn:focus {
outline: none; /* Removes the blue border that appears when the button is focused */
}
.dropdown {
position: relative;
display: inline-block;
}
.dropdown-content {
/* display: none; */
position: absolute;
right: 0;
text-align: end;
color: var(--button-secondary-color);
background-color: var(--text-color-subtile-2);
border-radius: 4px 4px 4px 4px;
min-width: 160px;
box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
z-index: 1;
/* Verstecke den Inhalt sofort */
opacity: 0;
visibility: hidden;
/* übergangsverzögerung für das Verschwinden */
transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out;
transition-delay: 0.2s;
}
#dropdown-content {transition-timing-function: ease;}
.dropdown-content:hover {
background-color: var(--text-color-subtile-2);
}
.dropdown-content a {
color: var(--border-color-2);
padding: 12px 16px;
border-radius: 4px 4px 4px 4px;
text-decoration: none;
display: block;
background-color: var(--text-color-subtile-2);
}
.dropdown-content a:hover {
color: var(--border-color-2);
background-color: var(--text-color-subtile-1);
font-weight: 600;
}
.dropdown:hover .dropdown-content {
/* display: block; */
border-radius: 4px 4px 4px 4px;
/* Übergang ohne Verzögerung für das Erscheinen */
opacity: 1;
visibility: visible;
transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s;
}
.dropdown:hover .dropbtn {
color: var(--button-primary-text);
background-color: var(--button-primary-color);
border: 1px solid var(--button-primary-border);
font-size: x-small;
font-weight: 600;
stroke: var(--button-primary-text);
}
.dropdown:hover .dropbtn svg{
stroke: var(--button-primary-text);
}
/* .dropdown:active .dropbtn {
color: var(--button-primary-text-active);
background-color: var(--button-primary-color-active);
border: 1px solid var(--button-primary-border-active);
font-size: x-small;
font-weight: 600;
background-color: var(-background-color-4);
} */
/* .omni {
display: flex;
justify-content: space-between;
align-items: center;
padding: 0.5em;
border: 1px solid var(--border-color-3);
border-radius: 5px;
margin: 0.5em 0;
} */

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export const systemPrompts = {
default: {
systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision."
},
empty: {
systemPrompt: ""
},
airoboros: {
systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request."
},
alpaca: {
systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request."
},
atlas: {
systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)."
},
atlas_de: {
systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)."
},
commandrempty: {
systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n"
},
commandrexample: {
systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available."
},
cot: {
systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query."
},
deduce: {
systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer."
},
deepseekcoder: {
systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer."
},
jordan: {
systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information."
},
leomistral: {
systemPrompt: "Du bist ein hilfreicher Assistent."
},
med42: {
systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE."
},
mistralopenorca: {
systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!"
},
migeltot: {
systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers."
},
orcamini: {
systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can."
},
samantha: {
systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha."
},
sauerkraut: {
systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten."
},
scarlett: {
systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI."
},
synthia: {
systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
},
vicuna: {
systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input."
},
};

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tools/server/public_legacy/theme-beeninorder.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration was a batman wallpaper that i have on my phone */
.theme-beeninorder {
--primary-color-1: hsl(202, 11%, 19%);
--primary-color-2: hsl(202, 11%, 23%);
--primary-color-3: hsl(201, 11%, 28%);
--primary-color-4: hsl(201, 11%, 40%);
--secondary-color-1: hsl(201, 11%, 80%);
--secondary-color-2: hsl(201, 11%, 74%);
--secondary-color-3: hsl(201, 11%, 67%);
--secondary-color-4: hsl(201, 11%, 60%);
--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%);
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(201, 11%, 19%);
--primary-color-1-hue: 201;
--primary-color-1-saturation: 11%;
--primary-color-1-lightness: 19%;
--primary-color-2: hsl(201, 11%, 23%);
--primary-color-2-hue: 201;
--primary-color-2-saturation: 11%;
--primary-color-2-lightness: 23%;
--primary-color-3: hsl(201, 11%, 28%);
--primary-color-3-hue: 201;
--primary-color-3-saturation: 11%;
--primary-color-3-lightness: 28%;
--primary-color-4: hsl(201, 11%, 40%);
--primary-color-4-hue: 201;
--primary-color-4-saturation: 11%;
--primary-color-4-lightness: 40%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(201, 11%, 80%);
--secondary-color-1-hue: 201;
--secondary-color-1-saturation: 11%;
--secondary-color-1-lightness: 80%;
--secondary-color-2: hsl(201, 11%, 74%);
--secondary-color-2-hue: 201;
--secondary-color-2-saturation: 11%;
--secondary-color-2-lightness: 74%;
--secondary-color-3: hsl(201, 11%, 67%);
--secondary-color-3-hue: 201;
--secondary-color-3-saturation: 11%;
--secondary-color-3-lightness: 67%;
--secondary-color-4: hsl(201, 11%, 60%);
--secondary-color-4-hue: 201;
--secondary-color-4-saturation: 11%;
--secondary-color-4-lightness: 60%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-1-hue: 44.5;
--theme-nuance-color-1-saturation: 96.7%;
--theme-nuance-color-1-lightness: 52.9%;
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-2-hue: 44.5;
--theme-nuance-color-2-saturation: 96.7%;
--theme-nuance-color-2-lightness: 52.9%;
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-3-hue: 44.5;
--theme-nuance-color-3-saturation: 96.7%;
--theme-nuance-color-3-lightness: 52.9%;
--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%);
--theme-nuance-color-4-hue: 44.5;
--theme-nuance-color-4-saturation: 96.7%;
--theme-nuance-color-4-lightness: 52.9%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(232, 40%, 45%);
--theme-orange-color: #e76f51;
--theme-yellow-color: #ffd95f;
--theme-green-color: #A3BE8C;
--theme-purple-color: hsl(232, 30%, 40%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--secondary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--secondary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--primary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(201,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text: var(--secondary-color-1);
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(44.5,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active: var(--secondary-color-1);
--button-secondary-color-active:
hsl(201,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(201,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
}

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tools/server/public_legacy/theme-ketivah.css Executable file
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/* Author: Yazan Agha-Schrader */
.theme-ketivah {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(0, 0%, 99.2%);
--primary-color-1-hue: 0;
--primary-color-1-saturation: 0%;
--primary-color-1-lightness: 99.2%;
--primary-color-2: hsl(0, 0%, 95%);
--primary-color-2-hue: 0;
--primary-color-2-saturation: 0%;
--primary-color-2-lightness: 95%;
--primary-color-3: hsl(0, 0%, 88%);
--primary-color-3-hue: 0;
--primary-color-3-saturation: 0%;
--primary-color-3-lightness: 88%;
--primary-color-4: hsl(0, 0%, 80%);
--primary-color-4-hue: 0;
--primary-color-4-saturation: 0%;
--primary-color-4-lightness: 80%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(0, 0%, 20%);
--secondary-color-1-hue: 0;
--secondary-color-1-saturation: 0%;
--secondary-color-1-lightness: 20%;
--secondary-color-2: hsl(0, 0%, 23.1%);
--secondary-color-2-hue: 0;
--secondary-color-2-saturation: 0%;
--secondary-color-2-lightness: 23.1%;
--secondary-color-3: hsl(0, 0%, 29%);
--secondary-color-3-hue: 0;
--secondary-color-3-saturation: 0%;
--secondary-color-3-lightness: 29%;
--secondary-color-4: hsl(0, 0.0%, 36.1%);
--secondary-color-4-hue: 0.0;
--secondary-color-4-saturation: 0.0%;
--secondary-color-4-lightness: 36.1%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(165.2, 0%, 35.1%);
--theme-nuance-color-1-hue: 165.2;
--theme-nuance-color-1-saturation: 82.1%;
--theme-nuance-color-1-lightness: 35.1%;
--theme-nuance-color-2: hsl(165.2, 0%, 35.1%);
--theme-nuance-color-2-hue: 165.2;
--theme-nuance-color-2-saturation: 82.1%;
--theme-nuance-color-2-lightness: 35.1%;
--theme-nuance-color-3: hsl(165.2, 0%, 35.3%);
--theme-nuance-color-3-hue: 165.2;
--theme-nuance-color-3-saturation: 81.1%;
--theme-nuance-color-3-lightness: 35.3%;
--theme-nuance-color-4: hsl(164.9, 0%, 27.6%);
--theme-nuance-color-4-hue: 164.9;
--theme-nuance-color-4-saturation: 81.6%;
--theme-nuance-color-4-lightness: 27.6%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(0.3, 80.0%, 50.0%);
--theme-orange-color: #e76f51;
--theme-yellow-color: hsl(60, 70.6%, 73.3%);
--theme-green-color: #A3BE8C;
--theme-purple-color: hsl(0.3, 70.0%, 45.0%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--primary-color-4);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 100%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 100%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
--loading-color-1: #eeeeee00;
--loading-color-2: #eeeeeeff;
}

216
tools/server/public_legacy/theme-mangotango.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */
.theme-mangotango {
--primary-color-1: hsl(192, 8.5%, 11.6%);
--primary-color-2: hsl(192, 8.5%, 21%);
--primary-color-3: hsl(192, 8.5%, 30%);
--primary-color-4: hsl(192, 8.5%, 40%);
--secondary-color-1: hsl(192, 8.5%, 80%);
--secondary-color-2: hsl(192, 8.5%, 73%);
--secondary-color-3: hsl(192, 8.5%, 66%);
--secondary-color-4: hsl(192, 8.5%, 60%);
--theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(192, 8.5%, 11.6%);
--primary-color-1-saturation: 8.5%;
--primary-color-1-lightness: 11.6%;
--primary-color-2: hsl(192, 8.5%, 21%);
--primary-color-2-saturation: 8.5%;
--primary-color-2-lightness: 21%;
--primary-color-3: hsl(192, 8.5%, 30%);
--primary-color-3-saturation: 8.5%;
--primary-color-3-lightness: 30%;
--primary-color-4: hsl(192, 8.5%, 40%);
--primary-color-4-saturation: 8.5%;
--primary-color-4-lightness: 40%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(192, 8.5%, 80%);
--secondary-color-1-saturation: 8.5%;
--secondary-color-1-lightness: 80%;
--secondary-color-2: hsl(192, 8.5%, 73%);
--secondary-color-2-saturation: 8.5%;
--secondary-color-2-lightness: 73%;
--secondary-color-3: hsl(192, 8.5%, 66%);
--secondary-color-3-saturation: 8.5%;
--secondary-color-3-lightness: 66%;
--secondary-color-4: hsl(192, 8.5%, 60%);
--secondary-color-4-saturation: 8.5%;
--secondary-color-4-lightness: 60%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-1-saturation: 100%;
--theme-nuance-color-1-lightness: 60.2%;
--theme-nuance-color-2: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-2-saturation: 100%;
--theme-nuance-color-2-lightness: 60.2%;
--theme-nuance-color-3: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-3-saturation: 100%;
--theme-nuance-color-3-lightness: 60.2%;
--theme-nuance-color-4: hsl(23.1, 100%, 60.2%);
--theme-nuance-color-4-saturation: 100%;
--theme-nuance-color-4-lightness: 60.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(325, 60%, 50%);
--theme-orange-color: #e76f51;
--theme-yellow-color: #ffd95f;
--theme-green-color: #A3BE8C;
--theme-blue-color: hsl(192, 95%, 40%);
--theme-purple-color: hsl(192, 80%, 35%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--secondary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--secondary-color-1);
--button-alert-color-active: var(--theme-blue-color);
--button-alert-border-active: var(--theme-blue-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--primary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(192,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text: var(--secondary-color-1);
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(23.1,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active: var(--secondary-color-1);
--button-secondary-color-active:
hsl(192,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(192,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
}

221
tools/server/public_legacy/theme-playground.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */
.theme-playground {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(0, 0%, 99.2%);
--primary-color-1-hue: 0;
--primary-color-1-saturation: 0%;
--primary-color-1-lightness: 99.2%;
--primary-color-2: hsl(0, 0%, 95%);
--primary-color-2-hue: 0;
--primary-color-2-saturation: 0%;
--primary-color-2-lightness: 95%;
--primary-color-3: hsl(0, 0%, 88%);
--primary-color-3-hue: 0;
--primary-color-3-saturation: 0%;
--primary-color-3-lightness: 88%;
--primary-color-4: hsl(0, 0%, 80%);
--primary-color-4-hue: 0;
--primary-color-4-saturation: 0%;
--primary-color-4-lightness: 80%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(0, 0%, 20%);
--secondary-color-1-hue: 0;
--secondary-color-1-saturation: 0%;
--secondary-color-1-lightness: 20%;
--secondary-color-2: hsl(0, 0%, 23.1%);
--secondary-color-2-hue: 0;
--secondary-color-2-saturation: 0%;
--secondary-color-2-lightness: 23.1%;
--secondary-color-3: hsl(0, 0%, 29%);
--secondary-color-3-hue: 0;
--secondary-color-3-saturation: 0%;
--secondary-color-3-lightness: 29%;
--secondary-color-4: hsl(0, 0%, 36.1%);
--secondary-color-4-hue: 0;
--secondary-color-4-saturation: 0%;
--secondary-color-4-lightness: 36.1%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%);
--theme-nuance-color-1-hue: 165.2;
--theme-nuance-color-1-saturation: 82.1%;
--theme-nuance-color-1-lightness: 35.1%;
--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%);
--theme-nuance-color-2-hue: 165.2;
--theme-nuance-color-2-saturation: 82.1%;
--theme-nuance-color-2-lightness: 35.1%;
--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%);
--theme-nuance-color-3-hue: 165.2;
--theme-nuance-color-3-saturation: 81.1%;
--theme-nuance-color-3-lightness: 35.3%;
--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%);
--theme-nuance-color-4-hue: 164.9;
--theme-nuance-color-4-saturation: 81.6%;
--theme-nuance-color-4-lightness: 27.6%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(0.3, 80%, 50%);
--theme-orange-color: #e76f51;
--theme-yellow-color: hsl(60, 70.6%, 73.3%);
--theme-green-color: #A3BE8C;
--theme-purple-color: hsl(0.3, 70%, 45%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--primary-color-4);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-purple-color);
--button-alert-border-hover: var(--theme-purple-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(0,
calc(var(--primary-color-1-saturation) - 100%),
calc(var(--primary-color-1-lightness) + 100%));
--button-primary-color-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 100%),
calc(var(--theme-nuance-color-3-lightness) + 100%));
--button-primary-color-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-primary-border-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color: var(--primary-color-3);
--button-secondary-border: var(--primary-color-3);
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover: var(--primary-color-4);
--button-secondary-border-hover: var(--primary-color-4);
/* ---------active--------- */
--button-secondary-text-active:
hsl(165.2,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
--button-secondary-border-active:
hsl(0,
calc(var(--primary-color-4-saturation) - 30%),
calc(var(--primary-color-4-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
/* ---------hover---------- */
--button-tertiary-text: var(--primary-color-4);
--button-tertiary-color: var(--primary-color-2);
--button-tertiary-border: var(--primary-color-2);
}

253
tools/server/public_legacy/theme-polarnight.css Executable file
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/* Author: Yazan Agha-Schrader */
/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
.theme-polarnight {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(220.0, 16.4%, 21.6%) ;
--primary-color-1-hue: 220.0;
--primary-color-1-saturation: 16.4%;
--primary-color-1-lightness: 21.6%;
--primary-color-2: hsl(221.7, 16.3%, 27.6%) ;
-primary-color-2-hue: 221.7;
--primary-color-2-saturation: 16.3%;
--primary-color-2-lightness: 27.6%;
--primary-color-3: hsl(220.0, 16.8%, 31.6%) ;
--primary-color-3-hue: 220.0;
--primary-color-3-saturation: 16.8%;
--primary-color-3-lightness: 31.6%;
--primary-color-4: hsl(220.0, 16.5%, 35.7%);
--primary-color-4-hue: 220.0;
--primary-color-4-saturation: 16.5%;
--primary-color-4-lightness: 35.7%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(217.5, 26.7%, 94.1%);
--secondary-color-1-hue: 217.5;
--secondary-color-1-saturation: 26.7%;
--secondary-color-1-lightness: 94.1%;
--secondary-color-2: hsl(218.2, 26.8%, 92.0%);
--secondary-color-2-hue: 218.2;
--secondary-color-2-saturation: 26.8%;
--secondary-color-2-lightness: 92.0%;
--secondary-color-3: hsl(218.8, 27.9%, 88.0%);
--secondary-color-3-hue: 218.8;
--secondary-color-3-saturation: 27.9%;
--secondary-color-3-lightness: 88.0%;
--secondary-color-4: hsl(218.8, 18.3%, 81.8%);
--secondary-color-4-hue: 218.8;
--secondary-color-4-saturation: 18.3%;
--secondary-color-4-lightness: 81.8%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
--theme-nuance-color-1-hue: 178.7;
--theme-nuance-color-1-saturation: 25.1%;
--theme-nuance-color-1-lightness: 64.9%;
--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
--theme-nuance-color-2-hue: 193.3;
--theme-nuance-color-2-saturation: 43.4%;
--theme-nuance-color-2-lightness: 67.5%;
--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
--theme-nuance-color-3-hue: 210.0;
--theme-nuance-color-3-saturation: 34.0%;
--theme-nuance-color-3-lightness: 63.1%;
--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
--theme-nuance-color-4-hue: 213.1;
--theme-nuance-color-4-saturation: 32.0%;
--theme-nuance-color-4-lightness: 52.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(354.3, 42.3%, 56.5%);
--theme-orange-color: hsl(20, 85%, 50%);
--theme-yellow-color: hsl(20, 75%, 45%);
--theme-green-color: hsl( 92.4, 27.8%, 64.7%);
--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
/* ------------------------------------------------ */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--secondary-color-1);
--button-alert-color-hover: var(--theme-yellow-color);
--button-alert-border-hover: var(--theme-yellow-color);
--button-alert-text-active: var(--secondary-color-1);
--button-alert-color-active: var(--theme-orange-color);
--button-alert-border-active: var(--theme-orange-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--secondary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(217.5,
calc(var(--secondary-color-1-saturation) - 35%),
calc(var(--secondary-color-1-lightness) + 30%));
--button-primary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 35%));
--button-primary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
--button-primary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
--button-secondary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
--button-secondary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
/* ---------active--------- */
--button-secondary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 25%));
--button-secondary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
--button-secondary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 15%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
/* ---------hover---------- */
--button-tertiary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
}

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/* Author: Yazan Agha-Schrader */
/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */
.theme-snowstorm {
/* ---------- PRIMARY COLORS ----------------- */
--primary-color-1: hsl(217.5, 26.7%, 94.1%);
--primary-color-1-hue: 217.5;
--primary-color-1-saturation: 26.7%;
--primary-color-1-lightness: 94.1%;
--primary-color-2: hsl(218.2, 26.8%, 92.0%);
--primary-color-2-hue: 218.2;
--primary-color-2-saturation: 26.8%;
--primary-color-2-lightness: 92.0%;
--primary-color-3: hsl(218.8, 27.9%, 88.0%);
--primary-color-3-hue: 218.8;
--primary-color-3-saturation: 27.9%;
--primary-color-3-lightness: 88.0%;
--primary-color-4: hsl(218.8, 18.3%, 81.8%);
--primary-color-4-hue: 218.8;
--primary-color-4-saturation: 18.3%;
--primary-color-4-lightness: 81.8%;
/* ---------- SECONDARY COLORS --------------- */
--secondary-color-1: hsl(220.0, 16.4%, 21.6%);
--secondary-color-1-hue: 220.0;
--secondary-color-1-saturation: 16.4%;
--secondary-color-1-lightness: 21.6%;
--secondary-color-2: hsl(221.7, 16.3%, 27.6%);
--secondary-color-2-hue: 221.7;
--secondary-color-2-saturation: 16.3%;
--secondary-color-2-lightness: 27.6%;
--secondary-color-3: hsl(220.0, 16.8%, 31.6%);
--secondary-color-3-hue: 220.0;
--secondary-color-3-saturation: 16.8%;
--secondary-color-3-lightness: 31.6%;
--secondary-color-4: hsl(220.0, 16.5%, 35.7%);
--secondary-color-4-hue: 220.0;
--secondary-color-4-saturation: 16.5%;
--secondary-color-4-lightness: 35.7%;
/* ----------- NUANCES COLORS ---------------- */
--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%);
--theme-nuance-color-1-hue: 178.7;
--theme-nuance-color-1-saturation: 25.1%;
--theme-nuance-color-1-lightness: 64.9%;
--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%);
--theme-nuance-color-2-hue: 193.3;
--theme-nuance-color-2-saturation: 43.4%;
--theme-nuance-color-2-lightness: 67.5%;
--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%);
--theme-nuance-color-3-hue: 210.0;
--theme-nuance-color-3-saturation: 34.0%;
--theme-nuance-color-3-lightness: 63.1%;
--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%);
--theme-nuance-color-4-hue: 213.1;
--theme-nuance-color-4-saturation: 32.0%;
--theme-nuance-color-4-lightness: 52.2%;
/* ----------- ROYGP COLORS ------------------ */
--theme-red-color: hsl(32.5, 80%, 50%);
--theme-orange-color: hsl(32.5, 70%, 45%);
--theme-yellow-color: hsl(40.0, 0.6%, 73.3%);
--theme-green-color: hsl(92.4, 27.8%, 64.7%);
--theme-purple-color: hsl(311.1, 20.2%, 63.1%);
/* ------------------------------------------- */
--background-color-1: var(--primary-color-1);
--background-color-2: var(--primary-color-2);
--background-color-3: var(--primary-color-3);
--background-color-4: var(--primary-color-4);
--border-color-1: var(--primary-color-2);
--border-color-2: var(--primary-color-3);
--border-color-3: var(--primary-color-4);
--border-focus-color: var(--theme-nuance-color-2);
--border-focus-shadow: var(--theme-nuance-color-1);
--text-color-plain: var(--secondary-color-1);
--text-color-subtile-1: var(--secondary-color-2);
--text-color-subtile-2: var(--secondary-color-3);
--code-background-color: var(--secondary-color-2);
--code-text-color: var(--primary-color-2);
--ui-range-thumb-color: var(--theme-nuance-color-3);
--ui-range-thumb-border: var(--ui-ranger-thumb-color);
--textarea-border-color: var(--secondary-color-4);
--chat-id-color: var(--theme-nuance-color-4);
/* ------------------------------------------- */
--button-alert-text-hover: var(--primary-color-1);
--button-alert-color-hover: var(--theme-orange-color);
--button-alert-border-hover: var(--theme-orange-color);
--button-alert-text-active: var(--primary-color-1);
--button-alert-color-active: var(--theme-red-color);
--button-alert-border-active: var(--theme-red-color);
/* ----------- PRIMARY BUTTONS --------------- */
/* - button should immediately catch the eye - */
--button-primary-text: var(--secondary-color-1);
--button-primary-color: var(--theme-nuance-color-3);
--button-primary-border: var(--theme-nuance-color-3);
/* ---------hover---------- */
--button-primary-text-hover:
hsl(217.5,
calc(var(--secondary-color-1-saturation) + 35%),
calc(var(--secondary-color-1-lightness) - 30%));
--button-primary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
--button-primary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 2%),
calc(var(--theme-nuance-color-3-lightness) - 10%));
/* ---------active--------- */
--button-primary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 35%));
--button-primary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
--button-primary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 10%),
calc(var(--theme-nuance-color-3-lightness) - 25%));
/* ---------- SECONDARY BUTTONS -------------- */
/* these should NOT immediately catch the eye */
--button-secondary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 50%));
--button-secondary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
--button-secondary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) + 10%));
/* ---------hover---------- */
--button-secondary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 20%),
calc(var(--theme-nuance-color-3-lightness) - 80%));
--button-secondary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
--button-secondary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 22%),
calc(var(--theme-nuance-color-3-lightness) + 1%));
/* ---------active--------- */
--button-secondary-text-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) + 40%),
calc(var(--theme-nuance-color-3-lightness) - 55%));
--button-secondary-color-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-secondary-border-active:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 30%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
/* ---------- TERTIARY BUTTONS --------------- */
/* ---------- disabled buttons --------------- */
--button-tertiary-text:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
/* ---------hover---------- */
--button-tertiary-text-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) - 5%));
--button-tertiary-color-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
--button-tertiary-border-hover:
hsl(210,
calc(var(--theme-nuance-color-3-saturation) - 40%),
calc(var(--theme-nuance-color-3-lightness) + 20%));
}

266
tools/server/public_simplechat/datautils.mjs Normal file
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//@ts-check
// Helpers to work with different data types
// by Humans for All
//
/**
* Given the limited context size of local LLMs and , many a times when context gets filled
* between the prompt and the response, it can lead to repeating text garbage generation.
* And many a times setting penalty wrt repeatation leads to over-intelligent garbage
* repeatation with slight variations. These garbage inturn can lead to overloading of the
* available model context, leading to less valuable response for subsequent prompts/queries,
* if chat history is sent to ai model.
*
* So two simple minded garbage trimming logics are experimented below.
* * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and
* * another based on char-histogram-driven garbage trimming.
* * in future characteristic of histogram over varying lengths could be used to allow for
* a more aggressive and adaptive trimming logic.
*/
/**
* Simple minded logic to help remove repeating garbage at end of the string.
* The repeatation needs to be perfectly matching.
*
* The logic progressively goes on probing for longer and longer substring based
* repeatation, till there is no longer repeatation. Inturn picks the one with
* the longest chain.
*
* @param {string} sIn
* @param {number} maxSubL
* @param {number} maxMatchLenThreshold
*/
export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) {
let rCnt = [0];
let maxMatchLen = maxSubL;
let iMML = -1;
for(let subL=1; subL < maxSubL; subL++) {
rCnt.push(0);
let i;
let refS = sIn.substring(sIn.length-subL, sIn.length);
for(i=sIn.length; i > 0; i -= subL) {
let curS = sIn.substring(i-subL, i);
if (refS != curS) {
let curMatchLen = rCnt[subL]*subL;
if (maxMatchLen < curMatchLen) {
maxMatchLen = curMatchLen;
iMML = subL;
}
break;
}
rCnt[subL] += 1;
}
}
console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt);
if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) {
return {trimmed: false, data: sIn};
}
console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen);
let iEnd = sIn.length - maxMatchLen;
return { trimmed: true, data: sIn.substring(0, iEnd) };
}
/**
* Simple minded logic to help remove repeating garbage at end of the string, till it cant.
* If its not able to trim, then it will try to skip a char at end and then trim, a few times.
* This ensures that even if there are multiple runs of garbage with different patterns, the
* logic still tries to munch through them.
*
* @param {string} sIn
* @param {number} maxSubL
* @param {number | undefined} [maxMatchLenThreshold]
*/
export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) {
let sCur = sIn;
let sSaved = "";
let iTry = 0;
while(true) {
let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold);
if (got.trimmed != true) {
if (iTry == 0) {
sSaved = got.data;
}
iTry += 1;
if (iTry >= skipMax) {
return sSaved;
}
got.data = got.data.substring(0,got.data.length-1);
} else {
iTry = 0;
}
sCur = got.data;
}
}
/**
* A simple minded try trim garbage at end using histogram driven characteristics.
* There can be variation in the repeatations, as long as no new char props up.
*
* This tracks the chars and their frequency in a specified length of substring at the end
* and inturn checks if moving further into the generated text from the end remains within
* the same char subset or goes beyond it and based on that either trims the string at the
* end or not. This allows to filter garbage at the end, including even if there are certain
* kind of small variations in the repeated text wrt position of seen chars.
*
* Allow the garbage to contain upto maxUniq chars, but at the same time ensure that
* a given type of char ie numerals or alphabets or other types dont cross the specified
* maxType limit. This allows intermixed text garbage to be identified and trimmed.
*
* ALERT: This is not perfect and only provides a rough garbage identification logic.
* Also it currently only differentiates between character classes wrt english.
*
* @param {string} sIn
* @param {number} maxType
* @param {number} maxUniq
* @param {number} maxMatchLenThreshold
*/
export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) {
if (sIn.length < maxMatchLenThreshold) {
return { trimmed: false, data: sIn };
}
let iAlp = 0;
let iNum = 0;
let iOth = 0;
// Learn
let hist = {};
let iUniq = 0;
for(let i=0; i<maxMatchLenThreshold; i++) {
let c = sIn[sIn.length-1-i];
if (c in hist) {
hist[c] += 1;
} else {
if(c.match(/[0-9]/) != null) {
iNum += 1;
} else if(c.match(/[A-Za-z]/) != null) {
iAlp += 1;
} else {
iOth += 1;
}
iUniq += 1;
if (iUniq >= maxUniq) {
break;
}
hist[c] = 1;
}
}
console.debug("DBUG:TrimHistGarbage:", hist);
if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) {
return { trimmed: false, data: sIn };
}
// Catch and Trim
for(let i=0; i < sIn.length; i++) {
let c = sIn[sIn.length-1-i];
if (!(c in hist)) {
if (i < maxMatchLenThreshold) {
return { trimmed: false, data: sIn };
}
console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i);
return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) };
}
}
console.debug("DBUG:TrimHistGarbage:Trimmed fully");
return { trimmed: true, data: "" };
}
/**
* Keep trimming repeatedly using hist_garbage logic, till you no longer can.
* This ensures that even if there are multiple runs of garbage with different patterns,
* the logic still tries to munch through them.
*
* @param {any} sIn
* @param {number} maxType
* @param {number} maxUniq
* @param {number} maxMatchLenThreshold
*/
export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) {
let sCur = sIn;
while (true) {
let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold);
if (!got.trimmed) {
return got.data;
}
sCur = got.data;
}
}
/**
* Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as
* skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying.
* @param {string} sIn
*/
export function trim_garbage_at_end(sIn) {
let sCur = sIn;
for(let i=0; i<2; i++) {
sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72);
sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12);
}
return sCur;
}
/**
* NewLines array helper.
* Allow for maintaining a list of lines.
* Allow for a line to be builtup/appended part by part.
*/
export class NewLines {
constructor() {
/** @type {string[]} */
this.lines = [];
}
/**
* Extracts lines from the passed string and inturn either
* append to a previous partial line or add a new line.
* @param {string} sLines
*/
add_append(sLines) {
let aLines = sLines.split("\n");
let lCnt = 0;
for(let line of aLines) {
lCnt += 1;
// Add back newline removed if any during split
if (lCnt < aLines.length) {
line += "\n";
} else {
if (sLines.endsWith("\n")) {
line += "\n";
}
}
// Append if required
if (lCnt == 1) {
let lastLine = this.lines[this.lines.length-1];
if (lastLine != undefined) {
if (!lastLine.endsWith("\n")) {
this.lines[this.lines.length-1] += line;
continue;
}
}
}
// Add new line
this.lines.push(line);
}
}
/**
* Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest]
* Optionally control whether only full lines (ie those with newline at end) will be returned
* or will a partial line without a newline at end (can only be the last line) be returned.
* @param {boolean} bFullWithNewLineOnly
*/
shift(bFullWithNewLineOnly=true) {
let line = this.lines[0];
if (line == undefined) {
return undefined;
}
if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){
return undefined;
}
return this.lines.shift();
}
}

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