convert : converting mmproj for Qwen2/2.5VL from convert_hf_to_gguf

convert_hf_to_gguf.py

@@ -1089,6 +1089,8 @@ def __init__(self, *args, **kwargs):
             raise TypeError("VisionModel must be subclassed with model_arch = gguf.MODEL_ARCH.CLIP_VISION")
 
         # get n_embd of the text model
+        if "text_config" not in self.hparams:
+            self.hparams["text_config"] = {}
         text_config = {**self.hparams, **self.hparams["text_config"]}
         self.n_embd_text = text_config.get("hidden_size", text_config.get("n_embd", 0))
         assert self.n_embd_text > 0, "n_embd not found in hparams"
@@ -2583,6 +2585,82 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@ModelBase.register("Qwen2VLForConditionalGeneration", "Qwen2_5_VLForConditionalGeneration")
+class Qwen2VLVisionModel(VisionModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.hparams["image_size"] = self.hparams.get("image_size", 560)
+        # rename config.json values
+        self.hparams["num_attention_heads"] = self.hparams.get("num_heads")
+        self.hparams["num_hidden_layers"] = self.hparams.get("depth")
+        if "embed_dim" in self.hparams: # qwen2vl
+            self.hparams["intermediate_size"] = self.hparams.get("hidden_size")
+            self.hparams["hidden_size"] = self.hparams.get("embed_dim")
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        if self.global_config['model_type'] == 'qwen2_vl':
+            self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN2VL)
+        elif self.global_config['model_type'] == 'qwen2_5_vl':
+            self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.QWEN25VL)
+            self.gguf_writer.add_vision_use_silu(True)
+            # find n_wa_pattern (window attention pattern)
+            fullatt_block_indexes = hparams.get("fullatt_block_indexes")
+            assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for qwen2_5_vl"
+            n_wa_pattern = fullatt_block_indexes[0] + 1
+            # validate n_wa_pattern
+            for i in range(1, len(fullatt_block_indexes)):
+                if fullatt_block_indexes[i] - fullatt_block_indexes[i - 1] != n_wa_pattern:
+                    raise ValueError(f"Invalid fullatt_block_indexes: {fullatt_block_indexes}")
+            self.gguf_writer.add_vision_n_wa_pattern(n_wa_pattern)
+        else:
+            raise ValueError(f"Unknown QwenVL model type: {self.global_config['model_type']}")
+        # default values below are taken from HF tranformers code
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.global_config.get("rms_norm_eps", 1e-6))
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del bid, name, n_dims  # unused
+        if ".patch_embd." in new_name:
+            return gguf.GGMLQuantizationType.F16
+        if ".position_embd." in new_name:
+            return gguf.GGMLQuantizationType.F32
+        return False
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+        if name.startswith("visual."):
+            # process visual tensors
+            # split QKV tensors if needed
+            if ".qkv." in name:
+                if data_torch.ndim == 2: # weight
+                    c3, _ = data_torch.shape
+                else: # bias
+                    c3 = data_torch.shape[0]
+                assert c3 % 3 == 0
+                c = c3 // 3
+                wq = data_torch[:c]
+                wk = data_torch[c: c * 2]
+                wv = data_torch[c * 2:]
+                return [
+                    (self.map_tensor_name(name.replace("qkv", "q")), wq),
+                    (self.map_tensor_name(name.replace("qkv", "k")), wk),
+                    (self.map_tensor_name(name.replace("qkv", "v")), wv),
+                ]
+            elif 'patch_embed.proj.weight' in name:
+                # split Conv3D into Conv2Ds
+                c1, c2, kt, kh, kw = data_torch.shape
+                del c1, c2, kh, kw  # unused
+                assert kt == 2, "Current implmentation only support temporal_patch_size of 2"
+                return [
+                    (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight"  , data_torch[:, :, 0, ...]),
+                    (gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.V_ENC_EMBD_PATCH] + ".weight.1", data_torch[:, :, 1, ...]),
+                ]
+            else:
+                return [(self.map_tensor_name(name), data_torch)]
+        return [] # skip other tensors
+
+
 @ModelBase.register("WavTokenizerDec")
 class WavTokenizerDecModel(TextModel):
     model_arch = gguf.MODEL_ARCH.WAVTOKENIZER_DEC

examples/llava/README.md

@@ -35,6 +35,16 @@ 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
 ```
@@ -60,7 +70,17 @@ Built upon `clip.cpp` (similar to `llava.cpp`), `libmtmd` offers several advanta
 
 ## How to obtain `mmproj`
 
-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:
+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)
@@ -70,10 +90,3 @@ Multimodal projector (`mmproj`) files are specific to each model architecture. P
 - [MiniCPM-o 2.6](../../docs/multimodal/minicpmo2.6.md)
 - [IBM Granite Vision](../../docs/multimodal/granitevision.md)
 - [Google Gemma 3](../../docs/multimodal/gemma3.md)
-
-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
-- [Mistral Small 3.1 24B](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503)

examples/llava/tests.sh

@@ -36,12 +36,6 @@ add_test() {
     arr_tmpl+=("$tmpl")
 }
 
-add_test_big() {
-    if [ "$RUN_BIG_TESTS" = true ]; then
-        add_test "$@"
-    fi
-}
-
 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"
@@ -58,8 +52,16 @@ 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
-add_test_big "llama-mtmd-cli" "ggml-org/pixtral-12b-GGUF:Q4_K_M"
-add_test_big "llama-mtmd-cli" "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7"
+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"