82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
import torch
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from typing import Literal, Optional
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from dataclasses import dataclass, field
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from huggingface_hub.hf_api import HfFolder
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@dataclass
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class ModelArguments:
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"""
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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=False,
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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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use_auth_token: Optional[bool] = field(
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default=False,
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metadata={"help": "Will use the token generated when running `huggingface-cli login`."}
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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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padding_side: Optional[Literal["left", "right"]] = field(
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default="left",
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metadata={"help": "The side on which the model should have padding applied."}
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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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compute_dtype: Optional[torch.dtype] = field(
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default=None,
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metadata={"help": "Used in quantization configs. Do not specify this argument manually."}
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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 delta model checkpoints as well as the configurations."}
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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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resume_lora_training: Optional[bool] = field(
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default=True,
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metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
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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": "Path to the directory containing the checkpoints of the reward model."}
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)
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def __post_init__(self):
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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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if self.use_auth_token == True and self.hf_hub_token != None:
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HfFolder.save_token(self.hf_hub_token)
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