ver4a/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity

llava : update documentations (#13055)

Browse source 
* llava : update documentations

* fix typo
This commit is contained in:
Xuan-Son Nguyen 2025-04-22 10:37:00 +02:00 • committed by GitHub
parent 1d735c0b4f
commit 243453533e
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
12 changed files with 212 additions and 491 deletions
Download patch file Download diff file Expand all files Collapse all files

177
examples/llava/README.md
View file

@ -1,158 +1,47 @@
# LLaVA
# Multimodal Support in llama.cpp
Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants,
as well as llava-1.6 [llava-v1.6](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2) variants.
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.
The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)
and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)
models are available.
For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](https://huggingface.co/cmp-nct/llava-1.6-gguf)
> [!IMPORTANT]
>
> Multimodal support can be viewed as a sub-project within `llama.cpp`. It is under **very heavy development**, and **breaking changes are expected**.
After API is confirmed, more models will be supported / uploaded.
The naming and structure related to multimodal support have evolved, which might cause some confusion. Here's a brief timeline to clarify:
## Usage
Build with cmake or run `make llama-llava-cli` to build it.
- [#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`.
After building, run: `./llama-llava-cli` to see the usage. For example:
## How it works and what is `mmproj`?
```sh
./llama-llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
```
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.
**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
**note**: For GPU offloading ensure to use the `-ngl` flag just like usual
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.
## LLaVA 1.5
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.
1. Clone a LLaVA and a CLIP model ([available options](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)). For example:
## What is `libmtmd`?
```sh
git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
As outlined in the history, `libmtmd` is the modern library designed to replace the original `llava.cpp` implementation for handling multimodal inputs.
git clone https://huggingface.co/openai/clip-vit-large-patch14-336
```
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.
2. Install the required Python packages:
## How to obtain `mmproj`
```sh
pip install -r examples/llava/requirements.txt
```
Multimodal projector (`mmproj`) files are specific to each model architecture. Please refer to the relevant guide for instructions on how to obtain or create them:
3. Use `llava_surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
```sh
python ./examples/llava/llava_surgery.py -m ../llava-v1.5-7b
```
4. Use `convert_image_encoder_to_gguf.py` to convert the LLaVA image encoder to GGUF:
```sh
python ./examples/llava/convert_image_encoder_to_gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
```
5. Use `examples/convert_legacy_llama.py` to convert the LLaMA part of LLaVA to GGUF:
```sh
python ./examples/convert_legacy_llama.py ../llava-v1.5-7b --skip-unknown
```
Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory.
## LLaVA 1.6 gguf conversion
1) First clone a LLaVA 1.6 model:
```console
git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
```
2) Install the required Python packages:
```sh
pip install -r examples/llava/requirements.txt
```
3) Use `llava_surgery_v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
```console
python examples/llava/llava_surgery_v2.py -C -m ../llava-v1.6-vicuna-7b/
```
- you will find a llava.projector and a llava.clip file in your model directory
4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
```console
mkdir vit
cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
cp ../llava-v1.6-vicuna-7b/llava.projector vit/
curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
```
5) Create the visual gguf model:
```console
python ./examples/llava/convert_image_encoder_to_gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
```
- This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
6) Then convert the model to gguf format:
```console
python ./examples/convert_legacy_llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown
```
7) And finally we can run the llava cli using the 1.6 model version:
```console
./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
```
**note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096)
**note** llava-1.6 greatly benefits from batched prompt processing (defaults work)
**note** if the language model in step `6)` is incompatible with the legacy conversion script, the easiest way handle the LLM model conversion is to load the model in transformers, and export only the LLM from the llava next model.
```python
import os
import transformers
model_path = ...
llm_export_path = ...
tokenizer = transformers.AutoTokenizer.from_pretrained(model_path)
model = transformers.AutoModelForImageTextToText.from_pretrained(model_path)
tokenizer.save_pretrained(llm_export_path)
model.language_model.save_pretrained(llm_export_path)
```
Then, you can convert the LLM using the `convert_hf_to_gguf.py` script, which handles more LLM architectures.
## llava-cli templating and llava-1.6 prompting
llava-1.5 models all use the same vicuna prompt, here you can just add your image question like `-p "Provide a full description."`
For llava-1.5 models which are not vicuna (mistral and Yi) you need to adapt system prompt as well as user prompt, for this purpose llava-cli has a basic templating system:
**For Mistral and using llava-cli binary:**
Add this: `-p "<image>\nUSER:\nProvide a full description.\nASSISTANT:\n"`
The mistral template for llava-1.6 seems to be no system print and a USER/ASSISTANT role
**For the 34B this should work:**
Add this: `-e -p <|im_start|>system\nAnswer the questions.<|im_end|><|im_start|>user\n<image>\nProvide a full description.<|im_end|><|im_start|>assistant\n`
## How to know if you are running in llava-1.5 or llava-1.6 mode
When running llava-cli you will see a visual information right before the prompt is being processed:
**Llava-1.5:**
`encode_image_with_clip: image embedding created: 576 tokens`
**Llava-1.6 (anything above 576):**
`encode_image_with_clip: image embedding created: 2880 tokens`
Alternatively just pay notice to how many "tokens" have been used for your prompt, it will also show 1000+ tokens for llava-1.6
## TODO
- [x] Support non-CPU backend for the image encoding part.
- [ ] Support different sampling methods.
- [ ] Support more model variants.
- [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)