mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-03 04:02:49 +08:00
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
import json
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import os
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from typing import Any, Dict, Optional
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import gradio as gr
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from peft.utils import WEIGHTS_NAME as PEFT_WEIGHTS_NAME
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from transformers.trainer import WEIGHTS_NAME, WEIGHTS_INDEX_NAME
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from llmtuner.extras.constants import SUPPORTED_MODELS
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DEFAULT_CACHE_DIR = "cache"
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DEFAULT_DATA_DIR = "data"
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DEFAULT_SAVE_DIR = "saves"
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USER_CONFIG = "user.config"
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DATA_CONFIG = "dataset_info.json"
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def get_save_dir(model_name: str) -> str:
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return os.path.join(DEFAULT_SAVE_DIR, os.path.split(model_name)[-1])
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def get_config_path() -> os.PathLike:
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return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG)
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def load_config() -> Dict[str, Any]:
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try:
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with open(get_config_path(), "r", encoding="utf-8") as f:
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return json.load(f)
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except:
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return {"lang": "", "last_model": "", "path_dict": {}}
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def save_config(lang: str, model_name: str, model_path: str) -> None:
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os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
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user_config = load_config()
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user_config["lang"] = lang or user_config["lang"]
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if model_name:
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user_config["last_model"] = model_name
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user_config["path_dict"][model_name] = model_path
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with open(get_config_path(), "w", encoding="utf-8") as f:
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json.dump(user_config, f, indent=2, ensure_ascii=False)
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def get_model_path(model_name: str) -> str:
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user_config = load_config()
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return user_config["path_dict"].get(model_name, SUPPORTED_MODELS.get(model_name, ""))
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def list_checkpoint(model_name: str, finetuning_type: str) -> Dict[str, Any]:
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checkpoints = []
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save_dir = os.path.join(get_save_dir(model_name), finetuning_type)
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if save_dir and os.path.isdir(save_dir):
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for checkpoint in os.listdir(save_dir):
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if (
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os.path.isdir(os.path.join(save_dir, checkpoint))
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and any([
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os.path.isfile(os.path.join(save_dir, checkpoint, name))
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for name in (WEIGHTS_NAME, WEIGHTS_INDEX_NAME, PEFT_WEIGHTS_NAME)
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])
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):
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checkpoints.append(checkpoint)
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return gr.update(value=[], choices=checkpoints)
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def load_dataset_info(dataset_dir: str) -> Dict[str, Any]:
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try:
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with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
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return json.load(f)
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except:
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return {}
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def list_dataset(dataset_dir: Optional[str] = None) -> Dict[str, Any]:
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dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
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return gr.update(value=[], choices=list(dataset_info.keys()))
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