mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-16 16:48:11 +08:00
76 lines
3.0 KiB
Python
76 lines
3.0 KiB
Python
import torch
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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from transformers import PreTrainedModel
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from llmtuner.extras.callbacks import LogCallback
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from llmtuner.extras.logging import get_logger
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from llmtuner.model import get_train_args, get_infer_args, load_model_and_tokenizer
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from llmtuner.train.pt import run_pt
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from llmtuner.train.sft import run_sft
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from llmtuner.train.rm import run_rm
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from llmtuner.train.ppo import run_ppo
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from llmtuner.train.dpo import run_dpo
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if TYPE_CHECKING:
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from transformers import TrainerCallback
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logger = get_logger(__name__)
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def run_exp(args: Optional[Dict[str, Any]] = None, callbacks: Optional[List["TrainerCallback"]] = None):
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model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
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callbacks = [LogCallback()] if callbacks is None else callbacks
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if finetuning_args.stage == "pt":
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run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
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elif finetuning_args.stage == "sft":
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run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
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elif finetuning_args.stage == "rm":
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run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
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elif finetuning_args.stage == "ppo":
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run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
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elif finetuning_args.stage == "dpo":
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run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
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else:
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raise ValueError("Unknown task.")
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def export_model(args: Optional[Dict[str, Any]] = None):
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model_args, _, finetuning_args, _ = get_infer_args(args)
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if model_args.adapter_name_or_path is not None and model_args.export_quantization_bit is not None:
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raise ValueError("Please merge adapters before quantizing the model.")
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model, tokenizer = load_model_and_tokenizer(model_args, finetuning_args)
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if getattr(model, "quantization_method", None) and model_args.adapter_name_or_path is not None:
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raise ValueError("Cannot merge adapters to a quantized model.")
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if not isinstance(model, PreTrainedModel):
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raise ValueError("The model is not a `PreTrainedModel`, export aborted.")
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model.config.use_cache = True
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if getattr(model.config, "torch_dtype", None) == "bfloat16":
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model = model.to(torch.bfloat16).to("cpu")
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else:
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model = model.to(torch.float16).to("cpu")
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setattr(model.config, "torch_dtype", "float16")
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model.save_pretrained(
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save_directory=model_args.export_dir,
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max_shard_size="{}GB".format(model_args.export_size),
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safe_serialization=(not model_args.export_legacy_format)
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)
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try:
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tokenizer.padding_side = "left" # restore padding side
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tokenizer.init_kwargs["padding_side"] = "left"
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tokenizer.save_pretrained(model_args.export_dir)
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except:
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logger.warning("Cannot save tokenizer, please copy the files manually.")
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if __name__ == "__main__":
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run_exp()
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