diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index c201883509ceb..f28ce00f08b46 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -2998,7 +2998,13 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter @ModelBase.register("InternVisionModel") class InternVisionModel(MmprojModel): def set_gguf_parameters(self): + assert self.hparams_vision is not None + 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"]) @@ -3022,14 +3028,30 @@ def tensor_force_quant(self, name, new_name, bid, n_dims): return gguf.GGMLQuantizationType.F32 return False + def _mapping_interns1_name(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", + } + if name in names_map: + name = names_map[name] + return name + 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"): + vision_prefix = ['vision_model', 'mlp', 'model.vision_tower', 'model.multi_modal_projector'] + # deal with intern-s1 special case + name = self._mapping_interns1_name(name) + if any([name.startswith(prefix) for prefix in vision_prefix]): # 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"): + if (".ls" in name or ".lambda_" in name or "position_embedding" in name) and not name.endswith(".weight"): name += ".weight" # split QKV tensors if needed if ".qkv." in name: @@ -3115,6 +3137,10 @@ 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") or name.startswith("model.vision_tower") or name.startswith("model.multi_modal_projector"): + # skip visual tensors + return [] if name.find("experts") != -1: n_experts = self.hparams["num_experts"] assert bid is not None @@ -3168,6 +3194,85 @@ class Qwen3Model(Qwen2Model): class Qwen3MoeModel(Qwen2MoeModel): model_arch = gguf.MODEL_ARCH.QWEN3MOE + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + hparams = ModelBase.load_hparams(self.dir_model) + self.origin_hf_arch = hparams.get('architectures', [None])[0] + + def set_vocab(self): + # deal with intern-s1 + if self.origin_hf_arch == 'InternS1ForConditionalGeneration': + self._set_vocab_interns1() + return + + try: + self._set_vocab_sentencepiece() + except FileNotFoundError: + self._set_vocab_gpt2() + + def _set_vocab_interns1(self): + tokens: list[str] = [] + toktypes: list[int] = [] + + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True) + vocab = getattr(tokenizer, 'vocab', tokenizer.get_vocab()) + vocab_size = self.hparams.get("vocab_size", len(vocab)) + assert max(vocab.values()) < vocab_size + + tokpre = self.get_vocab_base_pre(tokenizer) + + reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in vocab.items()} + added_vocab = tokenizer.get_added_vocab() + + added_tokens_decoder = tokenizer.added_tokens_decoder + + for i in range(vocab_size): + if i not in reverse_vocab: + tokens.append(f"[PAD{i}]") + toktypes.append(gguf.TokenType.UNUSED) + else: + token: str = reverse_vocab[i] + if token in added_vocab: + # The tokenizer in llama.cpp assumes the CONTROL and USER_DEFINED tokens are pre-normalized. + # To avoid unexpected issues - we make sure to normalize non-normalized tokens + if not added_tokens_decoder[i].normalized: + previous_token = token + token = tokenizer.decode(tokenizer.encode(token, add_special_tokens=False)) + if previous_token != token: + logger.info(f"{repr(previous_token)} is encoded and decoded back to {repr(token)} using AutoTokenizer") + + if added_tokens_decoder[i].special or self.does_token_look_special(token): + toktypes.append(gguf.TokenType.CONTROL) + else: + toktypes.append(gguf.TokenType.USER_DEFINED) + else: + toktypes.append(gguf.TokenType.NORMAL) + tokens.append(token) + + 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): diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 75855eba52c3c..ed622b746e494 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -1054,11 +1054,13 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_EMBD_CLS: ( "vision_tower.vision_model.embeddings.class_embedding", + "model.vision_tower.embeddings.cls_token", # Intern-S1 "vision_model.class_embedding", # llama 4 ), MODEL_TENSOR.V_ENC_EMBD_PATCH: ( "vision_tower.vision_model.embeddings.patch_embedding", + "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1 "vpm.embeddings.patch_embedding", "model.vision_model.embeddings.patch_embedding", # SmolVLM "vision_tower.patch_conv", # pixtral @@ -1068,6 +1070,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_EMBD_POS: ( "vision_tower.vision_model.embeddings.position_embedding", + "model.vision_tower.embeddings.position_embeddings", # Intern-S1 "vpm.embeddings.position_embedding", "model.vision_model.embeddings.position_embedding", # SmolVLM "vision_model.positional_embedding_vlm", # llama 4 @@ -1075,6 +1078,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_Q: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj", + "model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.q_proj", "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4 @@ -1084,10 +1088,12 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_Q_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1 ), MODEL_TENSOR.V_ENC_ATTN_K: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj", + "model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.k_proj", "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4 @@ -1097,10 +1103,12 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_K_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1 ), MODEL_TENSOR.V_ENC_ATTN_V: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj", + "model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.v_proj", "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4 @@ -1111,6 +1119,7 @@ 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 + "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1 "vpm.encoder.layers.{bid}.layer_norm1", "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral @@ -1121,6 +1130,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_ATTN_O: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj", "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1 "vpm.encoder.layers.{bid}.self_attn.out_proj", "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4 @@ -1131,6 +1141,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_POST_ATTN_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL + "model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1 "vpm.encoder.layers.{bid}.layer_norm2", "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4 @@ -1140,6 +1151,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_FFN_UP: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", + "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1 "vpm.encoder.layers.{bid}.mlp.fc1", "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral @@ -1155,6 +1167,7 @@ class TensorNameMap: MODEL_TENSOR.V_ENC_FFN_DOWN: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", + "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1 "vpm.encoder.layers.{bid}.mlp.fc2", "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral @@ -1165,10 +1178,12 @@ class TensorNameMap: MODEL_TENSOR.V_LAYER_SCALE_1: ( "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL + "model.vision_tower.encoder.layer.{bid}.lambda_1", # Intern-S1 ), MODEL_TENSOR.V_LAYER_SCALE_2: ( "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL + "model.vision_tower.encoder.layer.{bid}.lambda_2", # Intern-S1 ), MODEL_TENSOR.V_PRE_NORM: (