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convert : add BertForMaskedLM
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@ -2628,7 +2628,7 @@ class InternLM2Model(Model):
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return [(self.map_tensor_name(name), data_torch)]
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("BertModel", "CamembertModel")
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@Model.register("BertModel", "BertForMaskedLM", "CamembertModel")
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class BertModel(Model):
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class BertModel(Model):
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model_arch = gguf.MODEL_ARCH.BERT
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model_arch = gguf.MODEL_ARCH.BERT
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@ -2694,10 +2694,25 @@ class BertModel(Model):
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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del bid # unused
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if name.startswith("bert."):
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name = name[5:]
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if name.endswith(".gamma"):
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name = name[:-6] + ".weight"
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if name.endswith(".beta"):
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name = name[:-5] + ".bias"
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# we are only using BERT for embeddings so we don't need the pooling layer
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# we are only using BERT for embeddings so we don't need the pooling layer
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if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
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if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"):
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return [] # we don't need these
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return [] # we don't need these
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if name.startswith("cls.predictions"):
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return []
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if name.startswith("cls.seq_relationship"):
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return []
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return [(self.map_tensor_name(name), data_torch)]
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return [(self.map_tensor_name(name), data_torch)]
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