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convert : fix conversion for inheriting models that were bypassing modify_tensors (#19064)

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* Add undo_permute = False where needed

* Replace super().modify_tensors with ModelBase

* Add one more ModelBase.modify_tensors

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
Bartowski
2026-01-25 02:36:47 +01:00
committed by GitHub
co-authored by Sigbjørn Skjæret
parent e9fd8dcab4
commit 9981c30130
1 changed files with 11 additions and 11 deletions
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convert_hf_to_gguf.py
+11 -11
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@@ -2736,7 +2736,7 @@ class AfmoeModel(LlamaModel):
data_torch = torch.stack(datas, dim=0) data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid) yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
return return
else: else:
@@ -2745,7 +2745,7 @@ class AfmoeModel(LlamaModel):
if name.endswith(".expert_bias"): if name.endswith(".expert_bias"):
name = name.replace(".expert_bias", ".expert_bias.bias") name = name.replace(".expert_bias", ".expert_bias.bias")
yield from super().modify_tensors(data_torch, name, bid) yield from ModelBase.modify_tensors(self, data_torch, name, bid)
@ModelBase.register( @ModelBase.register(
@@ -8918,7 +8918,7 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
return Mamba2Model.modify_tensors(self, data_torch, name, bid) return Mamba2Model.modify_tensors(self, data_torch, name, bid)
elif bid in self._attn_layers: elif bid in self._attn_layers:
return GraniteMoeModel.modify_tensors(self, data_torch, name, bid) return GraniteMoeModel.modify_tensors(self, data_torch, name, bid)
yield from super().modify_tensors(data_torch, name, bid) yield from ModelBase.modify_tensors(self, data_torch, name, bid)
def set_gguf_parameters(self): def set_gguf_parameters(self):
"""This method merges params from both parents and some that are """This method merges params from both parents and some that are
@@ -9050,33 +9050,33 @@ class NemotronHModel(GraniteHybridModel):
if self.is_moe and bid is not None: if self.is_moe and bid is not None:
if name.endswith("mixer.gate.e_score_correction_bias"): if name.endswith("mixer.gate.e_score_correction_bias"):
new_name = name.replace("e_score_correction_bias", "e_score_correction.bias") new_name = name.replace("e_score_correction_bias", "e_score_correction.bias")
yield from super().modify_tensors(data_torch, new_name, bid) yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
return return
if name.endswith("mixer.dt_bias"): if name.endswith("mixer.dt_bias"):
new_name = name.replace("dt_bias", "dt.bias") new_name = name.replace("dt_bias", "dt.bias")
yield from super().modify_tensors(data_torch, new_name, bid) yield from ModelBase.modify_tensors(self, data_torch, new_name, bid)
return return
if name.endswith("mixer.conv1d.weight"): if name.endswith("mixer.conv1d.weight"):
squeezed_data = data_torch.squeeze() squeezed_data = data_torch.squeeze()
yield from super().modify_tensors(squeezed_data, name, bid) yield from ModelBase.modify_tensors(self, squeezed_data, name, bid)
return return
if name.endswith("mixer.A_log"): if name.endswith("mixer.A_log"):
transformed_data = -torch.exp(data_torch) transformed_data = -torch.exp(data_torch)
reshaped_data = transformed_data.squeeze().reshape(-1, 1) reshaped_data = transformed_data.squeeze().reshape(-1, 1)
yield from super().modify_tensors(reshaped_data, name, bid) yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
return return
if name.endswith("mixer.D"): if name.endswith("mixer.D"):
reshaped_data = data_torch.squeeze().reshape(-1, 1) reshaped_data = data_torch.squeeze().reshape(-1, 1)
yield from super().modify_tensors(reshaped_data, name, bid) yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
return return
if name.endswith("mixer.norm.weight"): if name.endswith("mixer.norm.weight"):
reshaped_data = data_torch.reshape(self.n_group, -1) reshaped_data = data_torch.reshape(self.n_group, -1)
yield from super().modify_tensors(reshaped_data, name, bid) yield from ModelBase.modify_tensors(self, reshaped_data, name, bid)
return return
if name.find("mixer.experts") != -1: if name.find("mixer.experts") != -1:
@@ -9101,7 +9101,7 @@ class NemotronHModel(GraniteHybridModel):
data_torch = torch.stack(datas, dim=0) data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
yield from super().modify_tensors(data_torch, merged_name, bid) yield from ModelBase.modify_tensors(self, data_torch, merged_name, bid)
return return
else: else:
return return
@@ -10731,7 +10731,7 @@ class CogVLMModel(LlamaModel):
if name.startswith("model.vision."): if name.startswith("model.vision."):
return return
yield from super().modify_tensors(data_torch, name, bid) yield from ModelBase.modify_tensors(self, data_torch, name, bid)
@ModelBase.register("JanusForConditionalGeneration") @ModelBase.register("JanusForConditionalGeneration")