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merged 6 commits into from
Aug 7, 2025
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Merged

Support intern-s1 #14875

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7cf5c4c
support internvl
RunningLeon Jul 16, 2025
859796e
support interns1
RunningLeon Jul 25, 2025
483ffef
resolve comments
RunningLeon Jul 28, 2025
5eba3e3
put interns1 in tensor mapping
RunningLeon Jul 30, 2025
c71543c
resolve comment
RunningLeon Jul 30, 2025
490a13f
move tokenizer changes to sub class
RunningLeon Aug 4, 2025
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109 changes: 107 additions & 2 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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"])
Expand All @@ -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:
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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()
Comment on lines +3208 to +3211
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Suggested change
try:
self._set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_gpt2()
super().set_vocab()


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)
Comment on lines +3259 to +3272
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Suggested change
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):
Expand Down
15 changes: 15 additions & 0 deletions gguf-py/gguf/tensor_mapping.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -1068,13 +1070,15 @@ 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
),

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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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
Expand All @@ -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: (
Expand Down