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from dataclasses import asdict, dataclass, field |
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from typing import Any, Dict, Literal, Optional |
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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 the model weight or identifier from huggingface.co/models or modelscope.cn/models."} |
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) |
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adapter_name_or_path: Optional[str] = field( |
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default=None, metadata={"help": "Path to the adapter weight or 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 pre-trained models downloaded from huggingface.co or modelscope.cn."}, |
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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 or not to use one of the fast tokenizer (backed by the tokenizers library)."}, |
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) |
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resize_vocab: Optional[bool] = field( |
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default=False, metadata={"help": "Whether or not to resize the tokenizer vocab and the embedding layers."} |
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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, 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", 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, metadata={"help": "Whether or not to use double quantization in int4 training."} |
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) |
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rope_scaling: Optional[Literal["linear", "dynamic"]] = field( |
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default=None, metadata={"help": "Which scaling strategy should be adopted for the RoPE embeddings."} |
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) |
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flash_attn: Optional[bool] = field( |
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default=False, 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, metadata={"help": "Enable shift short attention (S^2-Attn) proposed by LongLoRA."} |
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) |
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use_unsloth: Optional[bool] = field( |
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default=False, metadata={"help": "Whether or not to use unsloth's optimization for the LoRA training."} |
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) |
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disable_gradient_checkpointing: Optional[bool] = field( |
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default=False, metadata={"help": "Whether or not to disable gradient checkpointing."} |
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) |
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upcast_layernorm: Optional[bool] = field( |
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default=False, metadata={"help": "Whether or not to upcast the layernorm weights in fp32."} |
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) |
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upcast_lmhead_output: Optional[bool] = field( |
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default=False, metadata={"help": "Whether or not to upcast the output of lm_head in fp32."} |
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) |
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hf_hub_token: Optional[str] = field(default=None, metadata={"help": "Auth token to log in with Hugging Face Hub."}) |
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ms_hub_token: Optional[str] = field(default=None, metadata={"help": "Auth token to log in with ModelScope Hub."}) |
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export_dir: Optional[str] = field( |
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default=None, metadata={"help": "Path to the directory to save the exported model."} |
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) |
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export_size: Optional[int] = field( |
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default=1, metadata={"help": "The file shard size (in GB) of the exported model."} |
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) |
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export_quantization_bit: Optional[int] = field( |
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default=None, metadata={"help": "The number of bits to quantize the exported model."} |
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) |
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export_quantization_dataset: Optional[str] = field( |
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default=None, metadata={"help": "Path to the dataset or dataset name to use in quantizing the exported model."} |
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) |
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export_quantization_nsamples: Optional[int] = field( |
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default=128, metadata={"help": "The number of samples used for quantization."} |
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) |
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export_quantization_maxlen: Optional[int] = field( |
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default=1024, metadata={"help": "The maximum length of the model inputs used for quantization."} |
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) |
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export_legacy_format: Optional[bool] = field( |
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default=False, metadata={"help": "Whether or not to save the `.bin` files instead of `.safetensors`."} |
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) |
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export_hub_model_id: Optional[str] = field( |
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default=None, metadata={"help": "The name of the repository if push the model to the Hugging Face hub."} |
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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.adapter_name_or_path is not None: |
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self.adapter_name_or_path = [path.strip() for path in self.adapter_name_or_path.split(",")] |
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assert self.quantization_bit in [None, 8, 4], "We only accept 4-bit or 8-bit quantization." |
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assert self.export_quantization_bit in [None, 8, 4, 3, 2], "We only accept 2/3/4/8-bit quantization." |
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if self.export_quantization_bit is not None and self.export_quantization_dataset is None: |
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raise ValueError("Quantization dataset is necessary for exporting.") |
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def to_dict(self) -> Dict[str, Any]: |
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return asdict(self) |
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