mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-03 04:02:49 +08:00
89 lines
3.4 KiB
Python
89 lines
3.4 KiB
Python
from typing import Any, Dict, Literal, Optional
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from dataclasses import asdict, dataclass, field
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@dataclass
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class ModelArguments:
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r"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where to store the pretrained models downloaded from huggingface.co."}
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)
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use_fast_tokenizer: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}
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)
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split_special_tokens: Optional[bool] = field(
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default=False,
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metadata={"help": "Whether or not the special tokens should be split during the tokenization process."}
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)
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model_revision: Optional[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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quantization_bit: Optional[int] = field(
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default=None,
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metadata={"help": "The number of bits to quantize the model."}
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)
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quantization_type: Optional[Literal["fp4", "nf4"]] = field(
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default="nf4",
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metadata={"help": "Quantization data type to use in int4 training."}
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)
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double_quantization: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether to use double quantization in int4 training or not."}
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)
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rope_scaling: Optional[Literal["linear", "dynamic"]] = field(
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default=None,
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metadata={"help": "Adopt scaled rotary positional embeddings."}
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)
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checkpoint_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the directory(s) containing the model checkpoints as well as the configurations."}
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)
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flash_attn: Optional[bool] = field(
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default=False,
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metadata={"help": "Enable FlashAttention-2 for faster training."}
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)
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shift_attn: Optional[bool] = field(
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default=False,
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metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."}
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)
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reward_model: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
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)
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plot_loss: Optional[bool] = field(
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default=False,
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metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
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)
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hf_hub_token: Optional[str] = field(
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default=None,
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metadata={"help": "Auth token to log in with Hugging Face Hub."}
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)
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export_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Path to the directory to save the exported model."}
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)
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def __post_init__(self):
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self.compute_dtype = None
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self.model_max_length = None
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if self.split_special_tokens and self.use_fast_tokenizer:
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raise ValueError("`split_special_tokens` is only supported for slow tokenizers.")
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if self.checkpoint_dir is not None: # support merging multiple lora weights
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self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
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if self.quantization_bit is not None:
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assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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