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convert : fix Gemma3N, GraniteMoe and Ernie4.5Moe #19084

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Merged
CISC merged 2 commits into from
Jan 25, 2026
Merged

convert : fix Gemma3N, GraniteMoe and Ernie4.5Moe #19084

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85f9a03
fix Gemma3N and Ernie4.5Moe
CISC Jan 25, 2026
b727c81
fix GraniteMoe
CISC Jan 25, 2026
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24 changes: 12 additions & 12 deletions convert_hf_to_gguf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3799,7 +3799,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid)
else:
yield from super().modify_tensors(data_torch, name, bid)
yield from ModelBase.modify_tensors(self, data_torch, name, bid)

def prepare_tensors(self):
super().prepare_tensors()
Expand Down Expand Up @@ -6153,7 +6153,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
if new_name.endswith("conv_stem.conv.bias") or new_name.endswith("layer_scale.gamma"):
data_torch = data_torch.unsqueeze(0).unsqueeze(-1).unsqueeze(-1) # [1, C, 1, 1]

yield from super().modify_tensors(data_torch, new_name, bid)
yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)


@ModelBase.register("Gemma3nForCausalLM", "Gemma3nForConditionalGeneration")
Expand Down Expand Up @@ -6253,7 +6253,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter

# Continue with normal processing
name = name.replace("language_model.", "")
yield from super().modify_tensors(data_torch, name, bid)
yield from ModelBase.modify_tensors(self, data_torch, name, bid)
return

if "altup_unembed_projections" in name:
Expand All @@ -6270,7 +6270,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
raise ValueError(f"Unknown name: {name}")
out = self._stack_matrices(self._altup_unembd)
if out is not None:
yield from super().modify_tensors(out, "model.altup_unembed_projections.weight", bid)
yield from ModelBase.modify_tensors(self, out, "model.altup_unembed_projections.weight", bid)
return
else:
return
Expand All @@ -6287,7 +6287,7 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
raise ValueError(f"Unknown name: {name}")
out = self._stack_matrices(self._altup_proj)
if out is not None:
yield from super().modify_tensors(out, "model.altup_projections.weight", bid)
yield from ModelBase.modify_tensors(self, out, "model.altup_projections.weight", bid)
return
else:
return
Expand Down Expand Up @@ -8803,8 +8803,8 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
ffn_dim = self.hparams["intermediate_size"]
assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * intermediate_size"
gate, up = data_torch.split(ffn_dim, dim=-2)
yield from super().modify_tensors(gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
yield from super().modify_tensors(up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid)
yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_EXP, bid), bid)
yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_EXP, bid), bid)

has_experts = bool(self.hparams.get('num_local_experts'))

Expand All @@ -8813,15 +8813,15 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter
assert data_torch.shape[-2] == 2 * ffn_dim, "Merged FFN tensor size must be 2 * shared_intermediate_size"
gate, up = data_torch.split(ffn_dim, dim=-2)
if has_experts:
yield from super().modify_tensors(gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid)
yield from super().modify_tensors(up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid)
yield from ModelBase.modify_tensors(self, gate,self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE_SHEXP, bid), bid)
yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP_SHEXP, bid), bid)
return
yield from super().modify_tensors(gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
yield from super().modify_tensors(up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
yield from ModelBase.modify_tensors(self, gate, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), bid)
yield from ModelBase.modify_tensors(self, up, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), bid)
return

if not has_experts and name.endswith("shared_mlp.output_linear.weight"):
yield from super().modify_tensors(data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid)
yield from ModelBase.modify_tensors(self, data_torch, self.format_tensor_name(gguf.MODEL_TENSOR.FFN_DOWN, bid), bid)
return

yield from super().modify_tensors(data_torch, name, bid)
Expand Down