README.md

---
license: gemma
datasets:
- kunishou/databricks-dolly-15k-ja
language:
- ja
base_model:
- google/gemma-2-2b
library_name: transformers
tags:
- text-generation-inference
- transformers
---

## 出力方法
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import json
from tqdm import tqdm

def generate_task_outputs(input_jsonl_path, output_jsonl_path):
    # モデルとトークナイザーのロード
    model = AutoModelForCausalLM.from_pretrained(
        "google/gemma-2b-it",  # ベースモデル
        torch_dtype=torch.float16,
        device_map={"": 0}
    )
    tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")

    # LoRAアダプターの適用
    model = PeftModel.from_pretrained(
        model,
        "ユーザー名/リポジトリ名"
    )
    model.eval()

    # 入力データの読み込み
    tasks = []
    with open(input_jsonl_path, 'r') as f:
        for line in f:
            tasks.append(json.loads(line))

    # 出力の生成
    results = []
    for task in tqdm(tasks):
        input_text = task["input"]
        prompt = f"入力: {input_text}\n出力: "

        inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
        with torch.no_grad():
            outputs = model.generate(
                inputs.input_ids,
                max_length=512,
                temperature=0.7,
                do_sample=False,
                repetition_penalty=1.2
            )

        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        output_text = generated_text.split("出力: ")[-1].strip()

        results.append({
            "task_id": task["task_id"],
            "output": output_text
        })

    # 結果の保存
    with open(output_jsonl_path, 'w', encoding='utf-8') as f:
        for result in results:
            json.dump(result, f, ensure_ascii=False)
            f.write('\n')

# 使用例
input_file = "path/to/input.jsonl"
output_file = "path/to/output.jsonl"
generate_task_outputs(input_file, output_file)
```