Merge branch 'ggml-org:master' into feature/nemotron-h-support-working

Author: jwjohns

common/arg.cpp

@@ -1106,7 +1106,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
     printf("\"\n\n");
 
     printf("    case \"$prev\" in\n");
-    printf("        --model)\n");
+    printf("        --model|-m)\n");
     printf("            COMPREPLY=( $(compgen -f -X '!*.gguf' -- \"$cur\") $(compgen -d -- \"$cur\") )\n");
     printf("            return 0\n");
     printf("            ;;\n");

convert_hf_to_gguf.py

@@ -6254,9 +6254,11 @@ def prepare_tensors(self):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@ModelBase.register("DeepseekV2ForCausalLM")
-@ModelBase.register("DeepseekV3ForCausalLM")
-@ModelBase.register("KimiVLForConditionalGeneration")
+@ModelBase.register(
+    "DeepseekV2ForCausalLM",
+    "DeepseekV3ForCausalLM",
+    "KimiVLForConditionalGeneration",
+)
 class DeepseekV2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
 
@@ -8733,6 +8735,43 @@ def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", "
             return "mm.2.weight"
         return super().map_tensor_name(name, try_suffixes)
 
+
+@ModelBase.register("KimiVLForConditionalGeneration")
+class KimiVLModel(MmprojModel):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.hparams_vision is not None
+        self.hparams_vision["image_size"] = 64 * 14 # for compatibility
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.KIMIVL)
+        self.gguf_writer.add_vision_use_gelu(True)
+        self.gguf_writer.add_vision_projector_scale_factor(2)
+        # eps is the same as pytorch's default value
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams_vision.get("layer_norm_eps", 1e-5))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+        is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
+
+        if is_vision_tensor:
+            if "pos_emb.weight" in name:
+                data_torch = data_torch.view(data_torch.shape[0] * data_torch.shape[1], data_torch.shape[2])
+            elif "wqkv" in name:
+                split_dim = 0 if "weight" in name else -1
+                wq, wk, wv = data_torch.chunk(3, dim=split_dim)
+                return [
+                    (self.map_tensor_name(name.replace("wqkv", "wq")), wq),
+                    (self.map_tensor_name(name.replace("wqkv", "wk")), wk),
+                    (self.map_tensor_name(name.replace("wqkv", "wv")), wv)
+                ]
+
+            return [(self.map_tensor_name(name), data_torch)]
+
+        return [] # skip other tensors
+
 ###### CONVERSION LOGIC ######
 
 

docs/multimodal/minicpmv4.0.md

@@ -6,7 +6,7 @@ Download [MiniCPM-V-4](https://huggingface.co/openbmb/MiniCPM-V-4) PyTorch model
 
 
 ### Build llama.cpp
-Readme modification time: 20250206
+Readme modification time: 20250731
 
 If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
 

docs/multimodal/minicpmv4.5.md

@@ -0,0 +1,47 @@
+## MiniCPM-V 4.5
+
+### Prepare models and code
+
+Download [MiniCPM-V-4_5](https://huggingface.co/openbmb/MiniCPM-V-4_5) PyTorch model from huggingface to "MiniCPM-V-4_5" folder.
+
+
+### Build llama.cpp
+Readme modification time: 20250826
+
+If there are differences in usage, please refer to the official build [documentation](https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md)
+
+Clone llama.cpp:
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+```
+
+Build llama.cpp using `CMake`:
+```bash
+cmake -B build
+cmake --build build --config Release
+```
+
+
+### Usage of MiniCPM-V 4
+
+Convert PyTorch model to gguf files (You can also download the converted [gguf](https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf) by us)
+
+```bash
+python ./tools/mtmd/legacy-models/minicpmv-surgery.py -m ../MiniCPM-V-4_5
+python ./tools/mtmd/legacy-models/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-V-4_5 --minicpmv-projector ../MiniCPM-V-4_5/minicpmv.projector --output-dir ../MiniCPM-V-4_5/ --minicpmv_version 6
+python ./convert_hf_to_gguf.py ../MiniCPM-V-4_5/model
+
+# quantize int4 version
+./build/bin/llama-quantize ../MiniCPM-V-4_5/model/ggml-model-f16.gguf ../MiniCPM-V-4_5/model/ggml-model-Q4_K_M.gguf Q4_K_M
+```
+
+
+Inference on Linux or Mac
+```bash
+# run in single-turn mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_5/model/ggml-model-f16.gguf --mmproj ../MiniCPM-V-4_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"
+
+# run in conversation mode
+./build/bin/llama-mtmd-cli -m ../MiniCPM-V-4_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-V-4_5/mmproj-model-f16.gguf
+```

examples/model-conversion/Makefile

@@ -1,4 +1,5 @@
-# Validation functions
+MAKEFLAGS += --no-print-directory
+
 define validate_model_path
 	@if [ -z "$(MODEL_PATH)" ]; then \
 		echo "Error: MODEL_PATH must be provided either as:"; \
@@ -17,6 +18,13 @@ define validate_embedding_model_path
 	fi
 endef
 
+define quantize_model
+	@CONVERTED_MODEL="$(1)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" \
+	TOKEN_EMBD_TYPE="$(TOKEN_EMBD_TYPE)" OUTPUT_TYPE="$(OUTPUT_TYPE)" \
+	./scripts/utils/quantize.sh "$(1)" "$(QUANTIZED_TYPE)" "$(TOKEN_EMBD_TYPE)" "$(OUTPUT_TYPE)"
+	@echo "Export the quantized model path to $(2) variable in your environment"
+endef
+
 ###
 ### Casual Model targets/recipes
 ###
@@ -67,9 +75,15 @@ causal-quantize-Q8_0: causal-quantize-model
 causal-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
 causal-quantize-Q4_0: causal-quantize-model
 
+# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
+# token embedding and output types to Q8_0 instead of the default Q6_K.
+causal-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
+causal-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
+causal-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
+causal-quantize-qat-Q4_0: causal-quantize-model
+
 causal-quantize-model:
-	@CONVERTED_MODEL="$(CONVERTED_MODEL)" QUANTIZED_TYPE="$(QUANTIZED_TYPE)" ./scripts/utils/quantize.sh ${CONVERTED_MODEL} ${QUANTIZED_TYPE}
-	@echo "Export the quantized model path to QUANTIZED_MODEL variable in your environment"
+	$(call quantize_model,$(CONVERTED_MODEL),QUANTIZED_MODEL)
 
 causal-run-quantized-model:
 	@QUANTIZED_MODEL="$(QUANTIZED_MODEL)" ./scripts/causal/run-converted-model.sh ${QUANTIZED_MODEL}
@@ -117,9 +131,15 @@ embedding-quantize-Q8_0: embedding-quantize-model
 embedding-quantize-Q4_0: QUANTIZED_TYPE = Q4_0
 embedding-quantize-Q4_0: embedding-quantize-model
 
+# For Quantization Aware Trained (QAT) models in Q4_0 we explicitly set the
+# token embedding and output types to Q8_0 instead of the default Q6_K.
+embedding-quantize-qat-Q4_0: QUANTIZED_TYPE = Q4_0
+embedding-quantize-qat-Q4_0: TOKEN_EMBD_TYPE = Q8_0
+embedding-quantize-qat-Q4_0: OUTPUT_TYPE = Q8_0
+embedding-quantize-qat-Q4_0: embedding-quantize-model
+
 embedding-quantize-model:
-	@./scripts/utils/quantize.sh ${CONVERTED_EMBEDDING_MODEL} ${QUANTIZED_TYPE}
-	@echo "Export the quantized model path to QUANTIZED_EMBEDDING_MODEL variable in your environment"
+	$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
 
 embedding-run-quantized-model:
 	@./scripts/embedding/run-converted-model.sh ${QUANTIZED_EMBEDDING_MODEL}

examples/model-conversion/README.md

@@ -137,6 +137,18 @@ Then the quantized model can be run using the following command:
 (venv) $ make causal-run-quantized-model
 ```
 
+### Quantizing QAT (Quantization Aware Training) models
+When quantizing to `Q4_0`, the default data type for the token embedding weights
+will be `Q6_K`. For models that are going to be uploaded to ggml-org it is
+recommended to use `Q8_0` instead for the embeddings and output tensors.
+The reason is that although `Q6_K` is smaller in size, it requires more compute
+to unpack, which can hurt performance during output generation when the entire
+embedding matrix must be dequantized to compute vocabulary logits. `Q8_0`
+provides practically full quality with better computational efficiency.
+```console
+(venv) $ make causal-quantize-qat-Q4_0
+```
+
 
 ## Embedding Language Model Conversion
 
@@ -238,6 +250,18 @@ Then the quantized model can be run using the following command:
 (venv) $ make embedding-run-quantized-model
 ```
 
+### Quantizing QAT (Quantization Aware Training) models
+When quantizing to `Q4_0`, the default data type for the token embedding weights
+will be `Q6_K`. For models that are going to be uploaded to ggml-org it is
+recommended to use `Q8_0` instead for the embeddings and output tensors.
+The reason is that although `Q6_K` is smaller in size, it requires more compute
+to unpack, which can hurt performance during output generation when the entire
+embedding matrix must be dequantized to compute vocabulary logits. `Q8_0`
+provides practically full quality with better computational efficiency.
+```console
+(venv) $ make embedding-quantize-qat-Q4_0
+```
+
 ## Perplexity Evaluation
 
 ### Simple perplexity evaluation

examples/model-conversion/scripts/utils/quantize.sh

@@ -4,6 +4,8 @@ set -e
 
 CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"
 QUANTIZED_TYPE="${2:-"$QUANTIZED_TYPE"}"
+TOKEN_EMBD_TYPE="${3:-"${TOKEN_EMBD_TYPE}"}"
+OUTPUT_TYPE="${4:-"${OUTPUT_TYPE}"}"
 QUANTIZED_MODEL=$CONVERTED_MODEL
 
 # Final check if we have a model path
@@ -14,6 +16,11 @@ if [ -z "$CONVERTED_MODEL" ]; then
     exit 1
 fi
 
+if [ -z "$QUANTIZED_TYPE" ]; then
+    echo "Error: QUANTIZED_TYPE is required" >&2
+    exit 1
+fi
+
 echo $CONVERTED_MODEL
 
 # Process the quantized model filename
@@ -26,9 +33,16 @@ else
     exit 1
 fi
 
-
 cmake --build ../../build --target llama-quantize -j8
 
-../../build/bin/llama-quantize $CONVERTED_MODEL $QUANTIZED_MODEL $QUANTIZED_TYPE
+echo $TOKEN_EMBD_TYPE
+echo $OUTPUT_TYPE
+
+CMD_ARGS=("../../build/bin/llama-quantize")
+[[ -n "$TOKEN_EMBD_TYPE" ]] && CMD_ARGS+=("--token-embedding-type" "$TOKEN_EMBD_TYPE")
+[[ -n "$OUTPUT_TYPE" ]]     && CMD_ARGS+=("--output-tensor-type" "$OUTPUT_TYPE")
+CMD_ARGS+=("$CONVERTED_MODEL" "$QUANTIZED_MODEL" "$QUANTIZED_TYPE")
+
+"${CMD_ARGS[@]}"
 
 echo "Quantized model saved to: $QUANTIZED_MODEL"