llm-jp-3-13b-finetune

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This is the model that was made to solve Japanese tasks. It was fine-tuned by the data consisting of Japanese. llmのための日本語インストラクションデータ作成 is the data. In detail,ichikawa-instruction-003-001-1, ichikara-instruction-003-001-2.1.json, ichikara-instruction-003-001-2.2.json, ichikara-instruction-003-001-5.1.json, ichikara-instruction-003-001-5.2.json are the data In addition, this model has a following RAG function

MAX_TOKENS = 512

def classify_query(input_text): """クエリを分類して適切な指示を返す""" if "理由" in input_text: return "理由を順序だてて述べてください" elif "方法" in input_text: return "手順を具体的に説明してください" else: return "簡潔かつ正確に説明してください"

def postprocess_output(output_text): """生成された出力の後処理""" processed = re.sub(r"不要なフレーズ|テンプレート文", "", output_text) return processed.strip()

results = [] for data in tqdm(datasets): input_text = data["input"] instruction = classify_query(input_text)

prompt = f"""### 指示 ({instruction})(正しいことのみを語れ)
{input_text}
### 回答
"""

try:
    tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)

    if tokenized_input.size(1) > MAX_TOKENS:
        tokenized_input = tokenized_input[:, :MAX_TOKENS]

    attention_mask = torch.ones_like(tokenized_input)

    with torch.no_grad():
        outputs = model.generate(
            tokenized_input,
            attention_mask=attention_mask,
            max_new_tokens=100,
            do_sample=False,
            repetition_penalty=1.2,
            pad_token_id=tokenizer.eos_token_id
        )[0]

    output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
    output = postprocess_output(output)

    results.append({"task_id": data["task_id"], "input": input_text, "output": output})
    logging.info(f"Task {data['task_id']} completed successfully.")

except Exception as e:
    logging.error(f"Error for task_id {data['task_id']}: {str(e)}")
    results.append({"task_id": data["task_id"], "input": input_text, "output": f"Error: {str(e)}"})

JSONLファイルへの出力

jsonl_id = "example_model_id" # モデルIDを指定 with open(f"./{jsonl_id}-outputs.jsonl", 'w', encoding='utf-8') as f: for result in results: filtered_result = {key: result[key] for key in ["task_id", "output"] if key in result} json.dump(filtered_result, f, ensure_ascii=False) f.write('\n')

• Developed by: Taichi IGARASHI
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