mtmd : support InternVL 2.5 and 3 (ggml-org#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

Author: ngxson

convert_hf_to_gguf.py

@@ -426,7 +426,11 @@ def load_hparams(dir_model: Path):
             logger.warning(f"Failed to load model config from {dir_model}: {e}")
             logger.warning("Trying to load config.json instead")
             with open(dir_model / "config.json", "r", encoding="utf-8") as f:
-                return json.load(f)
+                config = json.load(f)
+                if "llm_config" in config:
+                    # rename for InternVL
+                    config["text_config"] = config["llm_config"]
+                return config
 
     @classmethod
     def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
@@ -2606,6 +2610,11 @@ def set_gguf_parameters(self):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if self.hf_arch == "Qwen2Model":
             name = f"model.{name}"  # map to Qwen2ForCausalLM tensors
+        if "language_model." in name:
+            name = name.replace("language_model.", "") # for InternVL
+        if name.startswith("mlp") or name.startswith("vision_model"):
+            # skip visual tensors
+            return []
         yield from super().modify_tensors(data_torch, name, bid)
 
 
@@ -2709,6 +2718,62 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         return [] # skip other tensors
 
 
+@ModelBase.register("InternVisionModel")
+class InternVisionModel(VisionModel):
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+        self.gguf_writer.add_vision_projector_type(gguf.VisionProjectorType.INTERNVL)
+        self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
+        # hidden_act
+        if hparams["hidden_act"] == "silu":
+            self.gguf_writer.add_vision_use_silu(True)
+        elif hparams["hidden_act"] == "gelu":
+            self.gguf_writer.add_vision_use_gelu(True)
+        else:
+            raise ValueError(f"Unsupported hidden_act: {hparams['hidden_act']}")
+        # downsample_ratio
+        downsample_ratio = self.global_config.get("downsample_ratio")
+        assert downsample_ratio is not None
+        self.gguf_writer.add_vision_projector_scale_factor(int(1.0 / downsample_ratio))
+
+    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("vision_model") or name.startswith("mlp"):
+            # process visual tensors
+            # correct name
+            if name.startswith("vision_model"):
+                name = "vision_tower." + name
+            if (".ls" in name or "position_embedding" in name) and not name.endswith(".weight"):
+                name += ".weight"
+            # 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("attn.qkv", "self_attn.q_proj")), wq),
+                    (self.map_tensor_name(name.replace("attn.qkv", "self_attn.k_proj")), wk),
+                    (self.map_tensor_name(name.replace("attn.qkv", "self_attn.v_proj")), wv),
+                ]
+            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
@@ -3360,6 +3425,11 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
         head_dim = n_embd // num_heads
         num_groups = num_heads // q_per_kv
 
+        name = name.replace("language_model.", "") # InternVL
+        if name.startswith("mlp") or name.startswith("vision_model"):
+            # skip visual tensors
+            return []
+
         if bid is not None and f"model.layers.{bid}.attention.wqkv" in name:
             qkv = data_torch
 
@@ -3433,6 +3503,10 @@ def set_gguf_parameters(self):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
+        name = name.replace("language_model.", "") # InternVL
+        if name.startswith("mlp") or name.startswith("vision_model"):
+            # skip visual tensors
+            return []
         if name.endswith(("q_proj.weight", "q_proj.bias")):
             data_torch = LlamaModel.permute(data_torch, n_head, n_head)
         if name.endswith(("k_proj.weight", "k_proj.bias")):

docs/multimodal.md

@@ -66,4 +66,12 @@ NOTE: some models may require large context window, for example: `-c 8192`
 
 # Mistral Small 3.1 24B (IQ2_M quantization)
 (tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF
+
+# InternVL 2.5 and 3
+(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF
+(tool_name) -hf ggml-org/InternVL2_5-2B-GGUF
+(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF
+(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
+(tool_name) -hf ggml-org/InternVL3-4B-Instruct-GGUF
+(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF
 ```

gguf-py/gguf/constants.py

@@ -491,6 +491,8 @@ class MODEL_TENSOR(IntEnum):
     V_ENC_FFN_UP         = auto()
     V_ENC_FFN_GATE       = auto()
     V_ENC_FFN_DOWN       = auto()
+    V_LAYER_SCALE_1      = auto()
+    V_LAYER_SCALE_2      = auto()
     V_PRE_NORM           = auto()
     V_POST_NORM          = auto()
     V_MM_INP_NORM        = auto()
@@ -748,6 +750,8 @@ class MODEL_TENSOR(IntEnum):
     MODEL_TENSOR.V_ENC_FFN_UP:              "v.blk.{bid}.ffn_up",
     MODEL_TENSOR.V_ENC_FFN_GATE:            "v.blk.{bid}.ffn_gate",
     MODEL_TENSOR.V_ENC_FFN_DOWN:            "v.blk.{bid}.ffn_down",
+    MODEL_TENSOR.V_LAYER_SCALE_1:           "v.blk.{bid}.ls1",
+    MODEL_TENSOR.V_LAYER_SCALE_2:           "v.blk.{bid}.ls2",
     MODEL_TENSOR.V_PRE_NORM:                "v.pre_ln",
     MODEL_TENSOR.V_POST_NORM:               "v.post_ln",
     MODEL_TENSOR.V_MM_INP_PROJ:             "mm.input_projection",
@@ -786,6 +790,8 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.V_ENC_FFN_UP,
         MODEL_TENSOR.V_ENC_FFN_GATE,
         MODEL_TENSOR.V_ENC_FFN_DOWN,
+        MODEL_TENSOR.V_LAYER_SCALE_1,
+        MODEL_TENSOR.V_LAYER_SCALE_2,
         MODEL_TENSOR.V_PRE_NORM,
         MODEL_TENSOR.V_POST_NORM,
         MODEL_TENSOR.V_MM_INP_PROJ,
@@ -2167,6 +2173,7 @@ class VisionProjectorType:
     PIXTRAL = "pixtral"
     QWEN2VL = "qwen2vl_merger"
     QWEN25VL = "qwen2.5vl_merger"
+    INTERNVL = "internvl"
 
 
 # Items here are (block size, type size)

gguf-py/gguf/tensor_mapping.py

@@ -905,6 +905,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MMPROJ_MLP: (
             "model.mm_projector.mlp.mlp.{bid}",
+            "mlp1.{bid}", # InternVL
         ),
 
         MODEL_TENSOR.V_MMPROJ_PEG: (
@@ -955,6 +956,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_INPUT_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1",
+            "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL
             "vpm.encoder.layers.{bid}.layer_norm1",
             "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM
             "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral
@@ -963,6 +965,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_OUTPUT: (
             "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj",
+            "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL
             "vpm.encoder.layers.{bid}.self_attn.out_proj",
             "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM
             "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral
@@ -971,6 +974,7 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_ENC_OUTPUT_NORM: (
             "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2",
+            "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL
             "vpm.encoder.layers.{bid}.layer_norm2",
             "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM
             "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral
@@ -1000,6 +1004,14 @@ class TensorNameMap:
             "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl
         ),
 
+        MODEL_TENSOR.V_LAYER_SCALE_1: (
+            "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL
+        ),
+
+        MODEL_TENSOR.V_LAYER_SCALE_2: (
+            "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL
+        ),
+
         MODEL_TENSOR.V_PRE_NORM: (
             "vision_tower.vision_model.pre_layrnorm",
             "vision_tower.ln_pre", # pixtral

tools/mtmd/README.md

@@ -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:
 

tools/mtmd/clip-impl.h

@@ -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) {