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from dataclasses import dataclass, field
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from typing import List, Optional
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from ..core import flatten_dict
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@dataclass
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class ModelConfig:
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"""
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Arguments which define the model and tokenizer to load.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={"help": ("The model checkpoint for weights initialization.")},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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torch_dtype: Optional[str] = field(
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default=None,
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metadata={
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"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."),
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"choices": ["auto", "bfloat16", "float16", "float32"],
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},
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)
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trust_remote_code: bool = field(default=False, metadata={"help": "Trust remote code when loading a model."})
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attn_implementation: Optional[str] = field(
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default=None,
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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`")},
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)
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use_peft: bool = field(
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default=False,
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metadata={"help": ("Whether to use PEFT or not for training.")},
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)
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lora_r: Optional[int] = field(
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default=16,
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metadata={"help": ("LoRA R value.")},
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)
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lora_alpha: Optional[int] = field(
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default=32,
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metadata={"help": ("LoRA alpha.")},
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)
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lora_dropout: Optional[float] = field(
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default=0.05,
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metadata={"help": ("LoRA dropout.")},
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)
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lora_target_modules: Optional[List[str]] = field(
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default=None,
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metadata={"help": ("LoRA target modules.")},
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)
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lora_modules_to_save: Optional[List[str]] = field(
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default=None,
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metadata={"help": ("Model layers to unfreeze & train")},
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)
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load_in_8bit: bool = field(default=False, metadata={"help": "use 8 bit precision for the base model - works only with LoRA"})
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load_in_4bit: bool = field(default=False, metadata={"help": "use 4 bit precision for the base model - works only with LoRA"})
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bnb_4bit_quant_type: Optional[str] = field(default="nf4", metadata={"help": "precise the quantization type (fp4 or nf4)"})
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use_bnb_nested_quant: bool = field(default=False, metadata={"help": "use nested quantization"})
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def to_dict(self):
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output_dict = {}
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for key, value in self.__dict__.items():
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output_dict[key] = value
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return flatten_dict(output_dict)
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def __post_init__(self):
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if self.load_in_8bit and self.load_in_4bit:
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raise ValueError("You can't use 8 bit and 4 bit precision at the same time")
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