mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-23 15:20:36 +08:00
58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Literal, Optional, TypedDict
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from peft import LoraConfig, PeftModel, get_peft_model
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from ...utils.plugin import BasePlugin
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from ...utils.types import HFModel
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class LoraConfigDict(TypedDict, total=False):
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name: Literal["lora"]
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"""Plugin name."""
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r: int
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"""Lora rank."""
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lora_alpha: int
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"""Lora alpha."""
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target_modules: list[str]
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"""Target modules."""
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class FreezeConfigDict(TypedDict, total=False):
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name: Literal["freeze"]
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"""Plugin name."""
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freeze_trainable_layers: int
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"""Freeze trainable layers."""
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freeze_trainable_modules: Optional[list[str]]
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"""Freeze trainable modules."""
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class PeftPlugin(BasePlugin):
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def __call__(self, model: HFModel, config: dict, is_train: bool) -> HFModel:
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return super().__call__(model, config)
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@PeftPlugin("lora").register
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def get_lora_model(model: HFModel, config: LoraConfigDict, is_train: bool) -> PeftModel:
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peft_config = LoraConfig(**config)
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model = get_peft_model(model, peft_config)
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return model
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@PeftPlugin("freeze").register
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def get_freeze_model(model: HFModel, config: FreezeConfigDict, is_train: bool) -> HFModel:
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raise NotImplementedError()
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