support interns1

Author: RunningLeon

convert_hf_to_gguf.py

@@ -420,9 +420,6 @@ def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]
         for filename in os.listdir(dir_model):
             if filename.startswith(prefix) and filename.endswith(suffix):
                 part_names.append(filename)
-            # TODO remove later
-            elif filename.endswith(suffix):
-                part_names.append(filename)
 
         part_names.sort()
 
@@ -3006,7 +3003,12 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
 @ModelBase.register("InternVisionModel")
 class InternVisionModel(MmprojModel):
     def set_gguf_parameters(self):
+        if isinstance(self.hparams_vision['image_size'], list):
+            self.hparams_vision['image_size'] = self.hparams_vision['image_size'][0]
+        if isinstance(self.hparams_vision['patch_size'], list):
+            self.hparams_vision['patch_size'] = self.hparams_vision['patch_size'][0]
         super().set_gguf_parameters()
+
         hparams = self.hparams
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.INTERNVL)
         self.gguf_writer.add_vision_attention_layernorm_eps(hparams["layer_norm_eps"])
@@ -3030,8 +3032,43 @@ def tensor_force_quant(self, name, new_name, bid, n_dims):
             return gguf.GGMLQuantizationType.F32
         return False
 
+    def _mapping_name_interns1(self, name):
+        names_map = {
+            "model.multi_modal_projector.layer_norm.bias": "mlp1.0.bias",
+            "model.multi_modal_projector.layer_norm.weight": "mlp1.0.weight",
+            "model.multi_modal_projector.linear_1.bias": "mlp1.1.bias",
+            "model.multi_modal_projector.linear_1.weight": "mlp1.1.weight",
+            "model.multi_modal_projector.linear_2.bias": "mlp1.3.bias",
+            "model.multi_modal_projector.linear_2.weight": "mlp1.3.weight",
+            "model.vision_tower.embeddings.cls_token": "vision_model.embeddings.class_embedding",
+            "model.vision_tower.embeddings.patch_embeddings.projection.bias": "vision_model.embeddings.patch_embedding.bias",
+            "model.vision_tower.embeddings.patch_embeddings.projection.weight": "vision_model.embeddings.patch_embedding.weight",
+            "model.vision_tower.embeddings.position_embeddings": "vision_model.embeddings.position_embedding",
+        }
+        if name in names_map:
+            name = names_map[name]
+        elif name.startswith("model.language_model."):
+            name = "language_model.model." + name[len("model.language_model.") :]
+        elif name.startswith("model.vision_tower."):
+            name = "vision_model." + name[len("model.vision_tower.") :]
+
+        if name.startswith("vision_model.encoder.layer"):
+            name = name.replace(r".layer.", r".layers.")
+            name = name.replace(r".attention.", r".attn.")
+            name = name.replace(r".attn.q_proj", r".self_attn.q_proj")
+            name = name.replace(r".attn.k_proj", r".self_attn.k_proj")
+            name = name.replace(r".attn.v_proj", r".self_attn.v_proj")
+            name = name.replace(r".projection_layer.", r".proj.")
+            name = name.replace(r".lambda_1", r".ls1")
+            name = name.replace(r".lambda_2", r".ls2")
+            name = name.replace(r".layernorm_before.", r".norm1.")
+            name = name.replace(r".layernorm_after.", r".norm2.")
+        return name
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
+        name = self._mapping_name_interns1(name)
+        # support interns1
         if name.startswith("vision_model") or name.startswith("mlp"):
             # process visual tensors
             # correct name
@@ -3123,8 +3160,8 @@ def set_gguf_parameters(self):
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         # process the experts separately
-        name = name.replace("language_model.", "") # InternVL
-        if name.startswith("mlp") or name.startswith("vision_model"):
+        name = name.replace(r"language_model.", r"") # InternVL
+        if name.startswith("mlp") or name.startswith("vision_model") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"):
             # skip visual tensors
             return []
         if name.find("experts") != -1:
@@ -3180,6 +3217,41 @@ class Qwen3Model(Qwen2Model):
 class Qwen3MoeModel(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3MOE
 
+    def set_vocab(self):
+        # deal with interns1
+        if 'interns1' in f'{self.dir_model}'.lower():
+            self._set_vocab_interns1()
+            return
+
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_gpt2()
+
+    def _set_vocab_interns1(self):
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_tokens_map_file = self.dir_model / 'special_tokens_map.json'
+        additional_special_tokens = []
+        if special_tokens_map_file.is_file():
+            with open(special_tokens_map_file, encoding = 'utf-8') as f:
+                    additional_special_tokens = json.load(f).get('additional_special_tokens', [])
+        tokenizer_cfg_file = self.dir_model / 'special_tokens_map.json'
+        if tokenizer_cfg_file.is_file():
+            with open(tokenizer_cfg_file, encoding = 'utf-8') as f:
+                    added_tokens_decoder = json.load(f).get('added_tokens_decoder', {})
+                    token2ids_map = {data['content'] : int(token) for token, data in added_tokens_decoder.items() if data['special']}
+                    for token in additional_special_tokens:
+                        if token in token2ids_map:
+                            special_vocab._set_special_token(token, token2ids_map[token])
+        special_vocab._set_special_token('eos', 151645)
+        special_vocab._set_special_token("bos", 151643)
+        special_vocab.add_to_gguf(self.gguf_writer)
 
 @ModelBase.register("GPT2LMHeadModel")
 class GPT2Model(TextModel):