src/llamafactory/model/model_utils/unsloth.py

# 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 TYPE_CHECKING, Any, Optional

from ...extras import logging
from ...extras.misc import get_current_device


if TYPE_CHECKING:
    from transformers import PretrainedConfig, PreTrainedModel

    from ...hparams import FinetuningArguments, ModelArguments


logger = logging.get_logger(__name__)


def _get_unsloth_kwargs(
    config: "PretrainedConfig",
    model_name_or_path: str,
    model_args: "ModelArguments",
    finetuning_args: "FinetuningArguments",
) -> dict[str, Any]:
    return {
        "model_name": model_name_or_path,
        "max_seq_length": model_args.model_max_length or 4096,
        "dtype": model_args.compute_dtype,
        "load_in_4bit": model_args.quantization_bit == 4,
        "token": model_args.hf_hub_token,
        "full_finetuning": finetuning_args.finetuning_type == "full",
        "device_map": {"": get_current_device()},
        "rope_scaling": getattr(config, "rope_scaling", None),
        "fix_tokenizer": False,
        "trust_remote_code": model_args.trust_remote_code,
        "use_gradient_checkpointing": "unsloth",
    }


def load_unsloth_pretrained_model(
    config: "PretrainedConfig", model_args: "ModelArguments", finetuning_args: "FinetuningArguments"
) -> Optional["PreTrainedModel"]:
    r"""Optionally load pretrained model with unsloth. Used in training."""
    from unsloth import FastLanguageModel  # type: ignore

    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.model_name_or_path, model_args, finetuning_args)
    try:
        model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
    except NotImplementedError:
        logger.warning_rank0("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))
        model = None
        model_args.use_unsloth = False

    return model


def get_unsloth_peft_model(
    model: "PreTrainedModel", model_args: "ModelArguments", peft_kwargs: dict[str, Any]
) -> "PreTrainedModel":
    r"""Get the peft model for the pretrained model with unsloth. Used in training."""
    from unsloth import FastLanguageModel  # type: ignore

    unsloth_peft_kwargs = {
        "model": model,
        "max_seq_length": model_args.model_max_length,
        "use_gradient_checkpointing": "unsloth",
    }
    return FastLanguageModel.get_peft_model(**peft_kwargs, **unsloth_peft_kwargs)


def load_unsloth_peft_model(
    config: "PretrainedConfig",
    model_args: "ModelArguments",
    finetuning_args: "FinetuningArguments",
    is_trainable: bool,
) -> "PreTrainedModel":
    r"""Load peft model with unsloth. Used in both training and inference."""
    from unsloth import FastLanguageModel  # type: ignore

    unsloth_kwargs = _get_unsloth_kwargs(config, model_args.adapter_name_or_path[0], model_args, finetuning_args)
    try:
        if not is_trainable:
            unsloth_kwargs["use_gradient_checkpointing"] = False

        model, _ = FastLanguageModel.from_pretrained(**unsloth_kwargs)
    except NotImplementedError:
        raise ValueError("Unsloth does not support model type {}.".format(getattr(config, "model_type", None)))

    if not is_trainable:
        FastLanguageModel.for_inference(model)

    return model