trl/trainer/model_config.py

from dataclasses import dataclass, field
from typing import List, Optional

from ..core import flatten_dict


@dataclass
class ModelConfig:
    """
    Arguments which define the model and tokenizer to load.
    """

    model_name_or_path: Optional[str] = field(
        default=None,
        metadata={"help": ("The model checkpoint for weights initialization.")},
    )
    model_revision: str = field(
        default="main",
        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
    )
    torch_dtype: Optional[str] = field(
        default=None,
        metadata={
            "help": ("Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the " "dtype will be automatically derived from the model's weights."),
            "choices": ["auto", "bfloat16", "float16", "float32"],
        },
    )
    trust_remote_code: bool = field(default=False, metadata={"help": "Trust remote code when loading a model."})
    attn_implementation: Optional[str] = field(
        default=None,
        metadata={"help": ("Which attention implementation to use; you can run --attn_implementation=flash_attention_2, in which case you must install this manually by running `pip install flash-attn --no-build-isolation`")},
    )
    use_peft: bool = field(
        default=False,
        metadata={"help": ("Whether to use PEFT or not for training.")},
    )
    lora_r: Optional[int] = field(
        default=16,
        metadata={"help": ("LoRA R value.")},
    )
    lora_alpha: Optional[int] = field(
        default=32,
        metadata={"help": ("LoRA alpha.")},
    )
    lora_dropout: Optional[float] = field(
        default=0.05,
        metadata={"help": ("LoRA dropout.")},
    )
    lora_target_modules: Optional[List[str]] = field(
        default=None,
        metadata={"help": ("LoRA target modules.")},
    )
    lora_modules_to_save: Optional[List[str]] = field(
        default=None,
        metadata={"help": ("Model layers to unfreeze & train")},
    )
    load_in_8bit: bool = field(default=False, metadata={"help": "use 8 bit precision for the base model - works only with LoRA"})
    load_in_4bit: bool = field(default=False, metadata={"help": "use 4 bit precision for the base model - works only with LoRA"})

    bnb_4bit_quant_type: Optional[str] = field(default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"})
    use_bnb_nested_quant: bool = field(default=False, metadata={"help": "use nested quantization"})

    def to_dict(self):
        output_dict = {}
        for key, value in self.__dict__.items():
            output_dict[key] = value
        return flatten_dict(output_dict)

    def __post_init__(self):
        if self.load_in_8bit and self.load_in_4bit:
            raise ValueError("You can't use 8 bit and 4 bit precision at the same time")