mtmd : support InternVL 2.5 and 3 (#13422)
* convert : internvl support * InternVL3-1B working * fix regression * rm mobilevlm from test * fix conversion * add test for internvl * add to list of pre-quant * restore boi/eoi check * add clarify comment for norm eps
This commit is contained in:
Xuan-Son Nguyen
GitHub
parent
d8919424f1
commit
053367d149
9 changed files with 243 additions and 25 deletions
|
|
@ -48,6 +48,7 @@ For the following models, you can use `convert_hf_to_gguf.py`with `--mmproj` fla
|
|||
- [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)
|
||||
- InternVL 2.5 and InternVL 3 from [OpenGVLab](https://huggingface.co/OpenGVLab) (note: we don't support conversion of `InternVL3-*-hf` model, only non-HF version is supported ; `InternLM2Model` **text** model is not supported)
|
||||
|
||||
For older models, please refer to the relevant guide for instructions on how to obtain or create them:
|
||||
|
||||
|
|
|
|||
|
|
@ -33,9 +33,6 @@
|
|||
#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"
|
||||
|
|
@ -60,8 +57,10 @@
|
|||
#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_1 "%s.blk.%d.ln1.%s" // layer norm
|
||||
#define TN_LN_2 "%s.blk.%d.ln2.%s" // layer norm
|
||||
#define TN_LS_1 "%s.blk.%d.ls1.%s" // layer scale
|
||||
#define TN_LS_2 "%s.blk.%d.ls2.%s" // layer scale
|
||||
#define TN_LN_PRE "%s.pre_ln.%s"
|
||||
#define TN_LN_POST "%s.post_ln.%s"
|
||||
#define TN_LLAVA_PROJ "mm.%d.%s"
|
||||
|
|
@ -105,6 +104,7 @@ enum projector_type {
|
|||
PROJECTOR_TYPE_IDEFICS3,
|
||||
PROJECTOR_TYPE_PIXTRAL,
|
||||
PROJECTOR_TYPE_QWEN25VL,
|
||||
PROJECTOR_TYPE_INTERNVL,
|
||||
PROJECTOR_TYPE_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -119,6 +119,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
|
|||
{ PROJECTOR_TYPE_GEMMA3, "gemma3"},
|
||||
{ PROJECTOR_TYPE_IDEFICS3, "idefics3"},
|
||||
{ PROJECTOR_TYPE_PIXTRAL, "pixtral"},
|
||||
{ PROJECTOR_TYPE_INTERNVL, "internvl"},
|
||||
};
|
||||
|
||||
static projector_type clip_projector_type_from_string(const std::string & str) {
|
||||
|
|
|
|||
|
|
@ -215,6 +215,10 @@ struct clip_layer {
|
|||
// layernorm 2
|
||||
ggml_tensor * ln_2_w = nullptr;
|
||||
ggml_tensor * ln_2_b = nullptr;
|
||||
|
||||
// layer scale (no bias)
|
||||
ggml_tensor * ls_1_w = nullptr;
|
||||
ggml_tensor * ls_2_w = nullptr;
|
||||
};
|
||||
|
||||
struct clip_vision_model {
|
||||
|
|
@ -589,6 +593,9 @@ struct clip_graph {
|
|||
|
||||
// Qwen2VL and Qwen2.5VL use M-RoPE
|
||||
ggml_cgraph * build_qwen2vl() {
|
||||
GGML_ASSERT(model.patch_bias == nullptr);
|
||||
GGML_ASSERT(model.class_embedding == nullptr);
|
||||
|
||||
const int batch_size = 1;
|
||||
const bool use_window_attn = hparams.n_wa_pattern > 0;
|
||||
const int n_wa_pattern = hparams.n_wa_pattern;
|
||||
|
|
@ -625,10 +632,6 @@ struct clip_graph {
|
|||
n_embd, n_patches_x * n_patches_y, batch_size);
|
||||
}
|
||||
|
||||
if (model.patch_bias) {
|
||||
inp = ggml_add(ctx0, inp, model.patch_bias);
|
||||
}
|
||||
|
||||
ggml_tensor * inpL = inp;
|
||||
ggml_tensor * window_mask = nullptr;
|
||||
ggml_tensor * window_idx = nullptr;
|
||||
|
|
@ -859,6 +862,67 @@ struct clip_graph {
|
|||
return gf;
|
||||
}
|
||||
|
||||
ggml_cgraph * build_internvl() {
|
||||
GGML_ASSERT(model.class_embedding != nullptr);
|
||||
GGML_ASSERT(model.position_embeddings != nullptr);
|
||||
|
||||
const int n_pos = n_patches + 1;
|
||||
ggml_tensor * inp = build_inp();
|
||||
|
||||
// add CLS token
|
||||
inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
|
||||
|
||||
ggml_tensor * cur = build_vit(
|
||||
inp, n_pos,
|
||||
NORM_TYPE_NORMAL,
|
||||
hparams.ffn_op,
|
||||
model.position_embeddings,
|
||||
nullptr);
|
||||
|
||||
// remove CLS token
|
||||
cur = ggml_view_2d(ctx0, cur,
|
||||
n_embd, n_patches,
|
||||
ggml_row_size(cur->type, n_embd), 0);
|
||||
|
||||
// pixel shuffle
|
||||
{
|
||||
const int scale_factor = model.hparams.proj_scale_factor;
|
||||
const int bsz = 1; // batch size, always 1 for now since we don't support batching
|
||||
const int height = n_patches_y;
|
||||
const int width = n_patches_x;
|
||||
GGML_ASSERT(scale_factor > 0);
|
||||
cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz);
|
||||
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
||||
cur = ggml_reshape_4d(ctx0, ggml_cont(ctx0, cur),
|
||||
n_embd * scale_factor * scale_factor,
|
||||
height / scale_factor,
|
||||
width / scale_factor,
|
||||
bsz);
|
||||
cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
|
||||
// flatten to 2D
|
||||
cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, cur),
|
||||
n_embd * scale_factor * scale_factor,
|
||||
cur->ne[1] * cur->ne[2]);
|
||||
}
|
||||
|
||||
// projector (always using GELU activation)
|
||||
{
|
||||
// projector LayerNorm uses pytorch's default eps = 1e-5
|
||||
// ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79
|
||||
cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1);
|
||||
cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
|
||||
cur = ggml_add(ctx0, cur, model.mm_1_b);
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
cur = ggml_mul_mat(ctx0, model.mm_3_w, cur);
|
||||
cur = ggml_add(ctx0, cur, model.mm_3_b);
|
||||
}
|
||||
|
||||
// build the graph
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
// this graph is used by llava, granite and glm
|
||||
// due to having embedding_stack (used by granite), we cannot reuse build_vit
|
||||
ggml_cgraph * build_llava() {
|
||||
|
|
@ -890,10 +954,6 @@ struct clip_graph {
|
|||
|
||||
ggml_tensor * inp = build_inp();
|
||||
|
||||
if (model.patch_bias) {
|
||||
inp = ggml_add(ctx0, inp, model.patch_bias);
|
||||
}
|
||||
|
||||
// concat class_embeddings and patch_embeddings
|
||||
if (model.class_embedding) {
|
||||
inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
|
||||
|
|
@ -1260,11 +1320,6 @@ private:
|
|||
ggml_tensor * learned_pos_embd,
|
||||
std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos
|
||||
) {
|
||||
if (model.patch_bias) {
|
||||
inp = ggml_add(ctx0, inp, model.patch_bias);
|
||||
cb(inp, "patch_bias", -1);
|
||||
}
|
||||
|
||||
if (learned_pos_embd) {
|
||||
inp = ggml_add(ctx0, inp, learned_pos_embd);
|
||||
cb(inp, "pos_embed", -1);
|
||||
|
|
@ -1324,6 +1379,11 @@ private:
|
|||
cb(cur, "attn_out", il);
|
||||
}
|
||||
|
||||
if (layer.ls_1_w) {
|
||||
cur = ggml_mul(ctx0, cur, layer.ls_1_w);
|
||||
cb(cur, "attn_out_scaled", il);
|
||||
}
|
||||
|
||||
// re-add the layer input, e.g., residual
|
||||
cur = ggml_add(ctx0, cur, inpL);
|
||||
|
||||
|
|
@ -1344,6 +1404,11 @@ private:
|
|||
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
if (layer.ls_2_w) {
|
||||
cur = ggml_mul(ctx0, cur, layer.ls_2_w);
|
||||
cb(cur, "ffn_out_scaled", il);
|
||||
}
|
||||
|
||||
// residual 2
|
||||
cur = ggml_add(ctx0, inpL, cur);
|
||||
cb(cur, "layer_out", il);
|
||||
|
|
@ -1365,6 +1430,10 @@ private:
|
|||
ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
|
||||
inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd);
|
||||
inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
|
||||
if (model.patch_bias) {
|
||||
inp = ggml_add(ctx0, inp, model.patch_bias);
|
||||
cb(inp, "patch_bias", -1);
|
||||
}
|
||||
return inp;
|
||||
}
|
||||
|
||||
|
|
@ -1627,6 +1696,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
|
|||
{
|
||||
res = graph.build_minicpmv();
|
||||
} break;
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
{
|
||||
res = graph.build_internvl();
|
||||
} break;
|
||||
default:
|
||||
{
|
||||
res = graph.build_llava();
|
||||
|
|
@ -1790,6 +1863,7 @@ struct clip_model_loader {
|
|||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_IDEFICS3:
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
{
|
||||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
|
||||
} break;
|
||||
|
|
@ -1897,6 +1971,9 @@ struct clip_model_loader {
|
|||
layer.o_w = get_tensor(string_format(TN_ATTN_OUTPUT, "v", il, "weight"));
|
||||
layer.ln_1_w = get_tensor(string_format(TN_LN_1, "v", il, "weight"), false);
|
||||
layer.ln_2_w = get_tensor(string_format(TN_LN_2, "v", il, "weight"), false);
|
||||
layer.ls_1_w = get_tensor(string_format(TN_LS_1, "v", il, "weight"), false); // no bias
|
||||
layer.ls_2_w = get_tensor(string_format(TN_LS_2, "v", il, "weight"), false); // no bias
|
||||
|
||||
layer.k_b = get_tensor(string_format(TN_ATTN_K, "v", il, "bias"), false);
|
||||
layer.q_b = get_tensor(string_format(TN_ATTN_Q, "v", il, "bias"), false);
|
||||
layer.v_b = get_tensor(string_format(TN_ATTN_V, "v", il, "bias"), false);
|
||||
|
|
@ -1904,7 +1981,7 @@ struct clip_model_loader {
|
|||
layer.ln_1_b = get_tensor(string_format(TN_LN_1, "v", il, "bias"), false);
|
||||
layer.ln_2_b = get_tensor(string_format(TN_LN_2, "v", il, "bias"), false);
|
||||
|
||||
// new naming
|
||||
// ffn
|
||||
layer.ff_up_w = get_tensor(string_format(TN_FFN_UP, "v", il, "weight"));
|
||||
layer.ff_up_b = get_tensor(string_format(TN_FFN_UP, "v", il, "bias"), false);
|
||||
layer.ff_gate_w = get_tensor(string_format(TN_FFN_GATE, "v", il, "weight"), false);
|
||||
|
|
@ -2052,6 +2129,15 @@ struct clip_model_loader {
|
|||
vision_model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM, false);
|
||||
vision_model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
{
|
||||
vision_model.mm_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
|
||||
vision_model.mm_0_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "bias"));
|
||||
vision_model.mm_1_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "weight"));
|
||||
vision_model.mm_1_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 1, "bias"));
|
||||
vision_model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
|
||||
vision_model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias"));
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false && "unknown projector type");
|
||||
}
|
||||
|
|
@ -2838,7 +2924,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
|
|||
}
|
||||
else if (ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE
|
||||
|| ctx->proj_type == PROJECTOR_TYPE_GEMMA3
|
||||
|| ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) {
|
||||
|| ctx->proj_type == PROJECTOR_TYPE_IDEFICS3
|
||||
|| ctx->proj_type == PROJECTOR_TYPE_INTERNVL // TODO @ngxson : support dynamic resolution
|
||||
) {
|
||||
clip_image_u8 resized_image;
|
||||
int sz = params.image_size;
|
||||
image_manipulation::resize_and_pad_image(*img, resized_image, {sz, sz});
|
||||
|
|
@ -2988,9 +3076,13 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
|
|||
|
||||
int n_patches = (params.image_size / params.patch_size) * (params.image_size / params.patch_size);
|
||||
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_LDP || ctx->proj_type == PROJECTOR_TYPE_LDPV2 || ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
|
||||
if (ctx->proj_type == PROJECTOR_TYPE_LDP
|
||||
|| ctx->proj_type == PROJECTOR_TYPE_LDPV2
|
||||
|| ctx->proj_type == PROJECTOR_TYPE_GLM_EDGE) {
|
||||
n_patches /= 4;
|
||||
n_patches += 2; // for BOI and EOI token embeddings
|
||||
if (ctx->vision_model.mm_glm_tok_boi) {
|
||||
n_patches += 2; // for BOI and EOI token embeddings
|
||||
}
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_MINICPMV) {
|
||||
if (ctx->minicpmv_version == 2) {
|
||||
n_patches = 96;
|
||||
|
|
@ -3013,7 +3105,8 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
|
|||
int n_per_side = params.image_size / params.patch_size;
|
||||
int n_per_side_2d_pool = n_per_side / params.proj_scale_factor;
|
||||
n_patches = n_per_side_2d_pool * n_per_side_2d_pool;
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3) {
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_IDEFICS3 || ctx->proj_type == PROJECTOR_TYPE_INTERNVL) {
|
||||
// both W and H are divided by proj_scale_factor
|
||||
n_patches /= (params.proj_scale_factor * params.proj_scale_factor);
|
||||
} else if (ctx->proj_type == PROJECTOR_TYPE_PIXTRAL) {
|
||||
int n_merge = params.spatial_merge_size;
|
||||
|
|
@ -3408,6 +3501,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
} break;
|
||||
case PROJECTOR_TYPE_GEMMA3:
|
||||
case PROJECTOR_TYPE_IDEFICS3:
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
{
|
||||
// do nothing
|
||||
} break;
|
||||
|
|
@ -3434,6 +3528,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
// the last node is the embedding tensor
|
||||
ggml_tensor * embeddings = ggml_graph_node(gf, -1);
|
||||
|
||||
// sanity check (only support batch size of 1 for now)
|
||||
const int n_tokens_out = embeddings->ne[1];
|
||||
const int expected_n_tokens_out = clip_n_output_tokens(ctx, imgs.entries[0].get());
|
||||
if (n_tokens_out != expected_n_tokens_out) {
|
||||
LOG_ERR("%s: expected %d tokens, got %d\n", __func__, expected_n_tokens_out, n_tokens_out);
|
||||
GGML_ABORT("Invalid number of output tokens");
|
||||
}
|
||||
|
||||
// copy the embeddings to the location passed by the user
|
||||
ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
|
||||
|
||||
|
|
@ -3604,6 +3706,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|||
return ctx->vision_model.mm_input_proj_w->ne[0];
|
||||
case PROJECTOR_TYPE_IDEFICS3:
|
||||
return ctx->vision_model.projection->ne[1];
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
return ctx->vision_model.mm_3_w->ne[1];
|
||||
default:
|
||||
GGML_ABORT("Unknown projector type");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -252,6 +252,13 @@ int32_t mtmd_tokenize(mtmd_context * ctx,
|
|||
|
||||
}
|
||||
|
||||
else if (proj_type == PROJECTOR_TYPE_INTERNVL) {
|
||||
// <img> ... (image embeddings) ... </img>
|
||||
marker_modified = "<img>" + ctx->image_marker + "</img>";
|
||||
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
|
||||
// for glm-edge, BOI and EOI token's embeddings are not present in the text model
|
||||
|
||||
|
|
|
|||
|
|
@ -40,7 +40,6 @@ 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"
|
||||
|
|
@ -50,6 +49,8 @@ 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"
|
||||
add_test "llama-mtmd-cli" "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
|
||||
add_test "llama-mtmd-cli" "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
|
||||
|
||||
# to test the big models, run: ./tests.sh big
|
||||
if [ "$RUN_BIG_TESTS" = true ]; then
|
||||
|
|
@ -59,6 +60,8 @@ if [ "$RUN_BIG_TESTS" = true ]; then
|
|||
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/InternVL3-8B-Instruct-GGUF:Q4_K_M"
|
||||
add_test "llama-mtmd-cli" "ggml-org/InternVL3-14B-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
|
||||
|
|
@ -70,6 +73,7 @@ fi
|
|||
|
||||
# this model has broken chat template, not usable
|
||||
# add_test "llama-mtmd-cli" "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
|
||||
# add_test "llama-mtmd-cli" "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
|
||||
|
||||
###############
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue