class GGUFModelLoader(BaseModelLoader):
"""
Model loader that can load GGUF files. This is useful for loading models
that are quantized with GGUF and saved in the GGUF format. This loader
supports loading both full models and sharded models.
"""
def __init__(self, load_config: LoadConfig):
super().__init__(load_config)
if load_config.model_loader_extra_config:
raise ValueError(
f"Model loader extra config is not supported for "
f"load format {load_config.load_format}"
)
def _prepare_weights(self, model_name_or_path: str):
if os.path.isfile(model_name_or_path):
return model_name_or_path
# for raw HTTPS link
if model_name_or_path.startswith(
("http://", "https://")
) and model_name_or_path.endswith(".gguf"):
return hf_hub_download(url=model_name_or_path)
# repo id/filename.gguf
if "/" in model_name_or_path and model_name_or_path.endswith(".gguf"):
repo_id, filename = model_name_or_path.rsplit("/", 1)
return hf_hub_download(repo_id=repo_id, filename=filename)
else:
raise ValueError(
f"Unrecognised GGUF reference: {model_name_or_path} "
"(expected local file, raw URL, or <repo_id>/<filename>.gguf)"
)
def _get_gguf_weights_map(self, model_config: ModelConfig):
"""
GGUF uses this naming convention for their tensors from HF checkpoint:
`blk.N.BB.weight` and `blk.N.BB.bias`
where N signifies the block number of a layer, and BB signifies the
attention/mlp layer components.
See "Standardized tensor names" in
https://github.com/ggerganov/ggml/blob/master/docs/gguf.md for details.
"""
config = model_config.hf_config
model_type = config.model_type
gguf_to_hf_name_map = {}
# hack: ggufs have a different name than transformers
if model_type == "cohere":
model_type = "command-r"
if model_type == "gemma3_text":
# Gemma3 models use "gemma3_text" in HuggingFace but
# "gemma3" in GGUF architecture naming
model_type = "gemma3"
if model_type in ("deepseek_v3", "deepseek_v2"):
model_type = "deepseek2"
# GGUF layer map assumes that we will have a merged expert weights
# so we need to map them manually
for idx in range(config.num_hidden_layers):
gguf_to_hf_name_map[f"blk.{idx}.exp_probs_b.bias"] = (
f"model.layers.{idx}.mlp.gate.e_score_correction_bias"
)
gguf_to_hf_name_map[f"blk.{idx}.ffn_down_exps.weight"] = (
f"model.layers.{idx}.mlp.experts.0.down_proj.weight"
)
gguf_to_hf_name_map[f"blk.{idx}.ffn_gate_exps.weight"] = (
f"model.layers.{idx}.mlp.experts.0.gate_proj.weight"
)
gguf_to_hf_name_map[f"blk.{idx}.ffn_up_exps.weight"] = (
f"model.layers.{idx}.mlp.experts.0.up_proj.weight"
)
if model_type in ("qwen2_moe", "qwen3_moe"):
model_type = model_type.replace("_", "")
# GGUF layer map assumes that we will have a merged expert weights
# so we need to map them manually
for idx in range(config.num_hidden_layers):
gguf_to_hf_name_map[f"blk.{idx}.ffn_down_exps.weight"] = (
f"model.layers.{idx}.mlp.experts.0.down_proj.weight"
)
gguf_to_hf_name_map[f"blk.{idx}.ffn_gate_exps.weight"] = (
f"model.layers.{idx}.mlp.experts.0.gate_proj.weight"
)
gguf_to_hf_name_map[f"blk.{idx}.ffn_up_exps.weight"] = (
f"model.layers.{idx}.mlp.experts.0.up_proj.weight"
)
arch = None
for key, value in gguf.MODEL_ARCH_NAMES.items():
if value == model_type:
arch = key
break
if arch is None:
raise RuntimeError(f"Unknown gguf model_type: {model_type}")
num_layers = config.num_hidden_layers
name_map = gguf.get_tensor_name_map(arch, num_layers)
with torch.device("meta"):
dummy_model = AutoModelForCausalLM.from_config(
config, trust_remote_code=model_config.trust_remote_code
)
state_dict = dummy_model.state_dict()
for hf_name in state_dict:
name, suffix = hf_name.rsplit(".", 1)
gguf_name = name_map.get_name(name)
gguf_to_hf_name_map[f"{gguf_name}.{suffix}"] = hf_name
return gguf_to_hf_name_map
def _get_weights_iterator(
self, model_name_or_path: str, gguf_to_hf_name_map: dict[str, str]
) -> Generator[tuple[str, torch.Tensor], None, None]:
return gguf_quant_weights_iterator(model_name_or_path, gguf_to_hf_name_map)
def download_model(self, model_config: ModelConfig) -> None:
self._prepare_weights(model_config.model)
def load_weights(self, model: nn.Module, model_config: ModelConfig) -> None:
local_model_path = self._prepare_weights(model_config.model)
gguf_weights_map = self._get_gguf_weights_map(model_config)
model.load_weights(
self._get_weights_iterator(local_model_path, gguf_weights_map)
)
def load_model(
self, vllm_config: VllmConfig, model_config: ModelConfig
) -> nn.Module:
device_config = vllm_config.device_config
local_model_path = self._prepare_weights(model_config.model)
gguf_weights_map = self._get_gguf_weights_map(model_config)
# we can only know if tie word embeddings after mapping weights
if "lm_head.weight" in get_gguf_extra_tensor_names(
local_model_path, gguf_weights_map
):
model_config.hf_config.update({"tie_word_embeddings": True})
weight_type_map = get_gguf_weight_type_map(model_config.model, gguf_weights_map)
# filter out unquantized modules to skip
unquant_names = [
name.removesuffix(".weight")
for name, weight_type in weight_type_map.items()
if weight_type == "F32" and name.endswith(".weight")
]
vllm_config.quant_config.unquantized_modules.extend(unquant_names)
target_device = torch.device(device_config.device)
with set_default_torch_dtype(model_config.dtype):
with target_device:
model = initialize_model(vllm_config=vllm_config)
self.load_weights(model, model_config)
process_weights_after_loading(model, model_config, target_device)
return model