modity code structure

Former-commit-id: 0682ed357210897e0b67c4a6eb31a94b3eb929f1

src/llmtuner/hparams/model_args.py

@@ -0,0 +1,72 @@
+import torch
+from typing import Literal, Optional
+from dataclasses import dataclass, field
+
+
+@dataclass
+class ModelArguments:
+    """
+    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=False,
+        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)."}
+    )
+    padding_side: Optional[Literal["left", "right"]] = field(
+        default="left",
+        metadata={"help": "The side on which the model should have padding applied."}
+    )
+    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."}
+    )
+    compute_dtype: Optional[torch.dtype] = field(
+        default=None,
+        metadata={"help": "Used in quantization configs. Do not specify this argument manually."}
+    )
+    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."}
+    )
+    resume_lora_training: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
+    )
+    plot_loss: Optional[bool] = field(
+        default=False,
+        metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
+    )
+
+    def __post_init__(self):
+        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."