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model : add support for ERNIE 4.5 0.3B model (#14408)

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Add Day-0 support for Baidu ERNIE 4.5 0.3B model.

Signed-off-by: Weizhao Ouyang <weizhao.ouyang@arm.com>
This commit is contained in:
Weizhao Ouyang
2025-06-28 22:08:21 +08:00
committed by GitHub
parent b25e92774e
commit 566c16fcce
6 changed files with 260 additions and 0 deletions
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46
convert_hf_to_gguf.py
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@ -2743,6 +2743,52 @@ class Qwen2Model(TextModel):
yield from super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Ernie4_5_ForCausalLM")
class Ernie4_5Model(TextModel):
model_arch = gguf.MODEL_ARCH.ERNIE4_5
def set_vocab(self):
self._set_vocab_sentencepiece()
def set_gguf_parameters(self):
super().set_gguf_parameters()
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
num_heads = self.hparams["num_attention_heads"]
num_kv_heads = self.hparams["num_key_value_heads"]
head_dim = self.hparams["head_dim"]
if "ernie." in name:
name = name.replace("ernie.", "model.")
# split the qkv weights
# qkv_proj shape: [(num_heads + 2 * num_kv_heads) * head_dim, hidden_size]
if "qkv_proj" in name:
name_q = name.replace("qkv_proj.weight", "q_proj.weight")
name_k = name.replace("qkv_proj.weight", "k_proj.weight")
name_v = name.replace("qkv_proj.weight", "v_proj.weight")
total_q_dim = num_heads * head_dim
total_k_dim = num_kv_heads * head_dim
total_v_dim = num_kv_heads * head_dim
q_proj_weight, k_proj_weight, v_proj_weight = data_torch.split([total_q_dim, total_k_dim, total_v_dim], dim=0)
return [
(self.map_tensor_name(name_q), q_proj_weight),
(self.map_tensor_name(name_k), k_proj_weight),
(self.map_tensor_name(name_v), v_proj_weight)
]
# split the up_gate_proj into gate and up
# up_gate_proj shape: [2 * intermediate_size, hidden_size]
if "up_gate_proj" in name:
name_up = name.replace("up_gate_proj.weight", "up_proj.weight")
name_gate = name.replace("up_gate_proj.weight", "gate_proj.weight")
dim_half = data_torch.shape[0] // 2
gate_proj_weight, up_proj_weight = data_torch.split(dim_half, dim=0)
return [
(self.map_tensor_name(name_gate), gate_proj_weight),
(self.map_tensor_name(name_up), up_proj_weight)
]
return [(self.map_tensor_name(name), data_torch)]
@ModelBase.register(
"Qwen2VLModel",
"Qwen2VLForConditionalGeneration",