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import torch
from typing import Literal, Optional
from dataclasses import dataclass, field
@dataclass
class ModelArguments:
r"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune.
"""
model_name_or_path: str = field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co."}
)
use_fast_tokenizer: Optional[bool] = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}
)
use_auth_token: Optional[bool] = field(
default=False,
metadata={"help": "Will use the token generated when running `huggingface-cli login`."}
)
model_revision: Optional[str] = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}
)
quantization_bit: Optional[int] = field(
default=None,
metadata={"help": "The number of bits to quantize the model."}
)
quantization_type: Optional[Literal["fp4", "nf4"]] = field(
default="nf4",
metadata={"help": "Quantization data type to use in int4 training."}
)
double_quantization: Optional[bool] = field(
default=True,
metadata={"help": "Whether to use double quantization in int4 training or not."}
)
rope_scaling: Optional[Literal["linear", "dynamic"]] = field(
default=None,
metadata={"help": "Adopt scaled rotary positional embeddings."}
)
flash_attn: Optional[bool] = field(
default=False,
metadata={"help": "Enable flash attention for faster training."}
)
checkpoint_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to the directory(s) containing the delta model checkpoints as well as the configurations."}
)
reward_model: Optional[str] = field(
default=None,
metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
)
plot_loss: Optional[bool] = field(
default=False,
metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
)
hf_auth_token: Optional[str] = field(
default=None,
metadata={"help": "Auth token to log in with Hugging Face Hub."}
)
def __post_init__(self):
self.compute_dtype = None
self.model_max_length = None
if self.checkpoint_dir is not None: # support merging multiple lora weights
self.checkpoint_dir = [cd.strip() for cd in self.checkpoint_dir.split(",")]
if self.quantization_bit is not None:
assert self.quantization_bit in [4, 8], "We only accept 4-bit or 8-bit quantization."
if self.use_auth_token == True and self.hf_auth_token is not None:
from huggingface_hub.hf_api import HfFolder # lazy load
HfFolder.save_token(self.hf_auth_token)
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