imagfff/llm-jp-3-13b-it

Uploaded model

• Developed by: imagfff
• License: apache-2.0
• Finetuned from model : llm-jp/llm-jp-3-13b

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

提出したjsonlファイルの出力方法

1. 必要なライブラリのインストール

pip install unsloth
pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

1. 下記のコードを実行

import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List

import torch
from tqdm import tqdm
from unsloth import FastLanguageModel


@dataclass
class ModelConfig:
    model_name: str = "imagfff/llm-jp-3-13b-it"
    max_seq_length: int = 2048
    dtype: Any = None
    load_in_4bit: bool = True
    token: str = "HF token"


def load_model(config: ModelConfig) -> tuple[Any, Any]:
    """モデルとトークナイザーを読み込む"""
    model, tokenizer = FastLanguageModel.from_pretrained(
        model_name=config.model_name,
        max_seq_length=config.max_seq_length,
        dtype=config.dtype,
        load_in_4bit=config.load_in_4bit,
        token=config.token,
    )
    FastLanguageModel.for_inference(model)
    return model, tokenizer


def load_datasets(file_path: str) -> List[Dict[str, Any]]:
    """JSONLファイルからデータセットを読み込む"""
    datasets = []
    try:
        with open(file_path) as f:
            item = ""
            for line in f:
                line = line.strip()
                item += line
                if item.endswith("}"):
                    datasets.append(json.loads(item))
                    item = ""
        return datasets
    except (FileNotFoundError, json.JSONDecodeError) as e:
        raise Exception(f"データセットの読み込みに失敗しました: {e}") from e


def generate_prediction(model: Any, tokenizer: Any, input_text: str) -> str:
    """モデルによる推論を実行"""
    prompt = f"### 指示\n{input_text}\n### 回答\n"
    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            use_cache=True,
            do_sample=False,
            repetition_penalty=1.2,
        )
    return tokenizer.decode(outputs[0], skip_special_tokens=True).split("\n### 回答")[
        -1
    ]


def save_results(results: List[Dict[str, Any]], output_path: str) -> None:
    """結果をJSONLファイルに保存"""
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    with open(output_path, "w", encoding="utf-8") as f:
        for result in results:
            json.dump(result, f, ensure_ascii=False)
            f.write("\n")


def main():
    config = ModelConfig()
    model, tokenizer = load_model(config)

    datasets = load_datasets("./elyza-tasks-100-TV_0.jsonl")

    results = []
    for dt in tqdm(datasets, desc="推論実行中"):
        prediction = generate_prediction(model, tokenizer, dt["input"])
        results.append(
            {"task_id": dt["task_id"], "input": dt["input"], "output": prediction}
        )

    model_basename = config.model_name.split("/")[-1]
    save_results(results, f"/content/{model_basename}_output.jsonl")


if __name__ == "__main__":
    main()