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db704cb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Literal, TypedDict
from peft import LoraConfig, PeftModel, get_peft_model
from ...utils.plugin import BasePlugin
from ...utils.types import HFModel
class LoraConfigDict(TypedDict, total=False):
name: Literal["lora"]
"""Plugin name."""
r: int
"""Lora rank."""
lora_alpha: int
"""Lora alpha."""
target_modules: list[str]
"""Target modules."""
class FreezeConfigDict(TypedDict, total=False):
name: Literal["freeze"]
"""Plugin name."""
freeze_trainable_layers: int
"""Freeze trainable layers."""
freeze_trainable_modules: list[str] | None
"""Freeze trainable modules."""
class PeftPlugin(BasePlugin):
def __call__(self, model: HFModel, config: dict, is_train: bool) -> HFModel:
return super().__call__(model, config)
@PeftPlugin("lora").register()
def get_lora_model(model: HFModel, config: LoraConfigDict, is_train: bool) -> PeftModel:
peft_config = LoraConfig(**config)
model = get_peft_model(model, peft_config)
return model
@PeftPlugin("freeze").register()
def get_freeze_model(model: HFModel, config: FreezeConfigDict, is_train: bool) -> HFModel:
raise NotImplementedError()
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