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n4/llm-jp-3-13b-finetune-10

sourceHugging Faceupdated 2y agoView on Hugging Face
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  • —License: 本モデルは、CC-BY-NC-SAライセンス下で利用可能なデータセットを用いて学習されています。そのため、本モデルを利用する際には、元データセットのライセンスに準拠する必要があります。
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Uses

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Direct Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

Google Colabで実行してください。

!pip install bitsandbytes

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import json
from tqdm import tqdm

# 必要な設定
model_name = "n4/llm-jp-3-13b-finetune-10"
max_seq_length = 1024
load_in_4bit = True  # 4-bit量子化を有効化

# モデルとトークナイザーのロード
print("モデルをロード中...")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.float16 if load_in_4bit else None

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=dtype,
    load_in_4bit=load_in_4bit,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
print("モデルのロードが完了しました。")
# 推論用ファイルの用意
elyza-tasks-100-TV_0.jsonl を /content/elyza-tasks-100-TV_0.jsonl となるようにアップロードしておいてください。

# データセットの読み込み
datasets = []
with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
    item = ""
    for i, line in enumerate(f):
        line = line.strip()
        item += line
        if item.endswith("}"):
            data = json.loads(item)
            # task_id がない場合は行番号を追加
            if "task_id" not in data:
                data["task_id"] = i  # 0から始まる行番号
            datasets.append(data)
            item = ""

# 推論
results = []
print("推論を開始します...")
for dt in tqdm(datasets):
    input_text = dt["input"]

    # プロンプト作成
    prompt = f"<s>指示を読んで、質問内容を把握してください。把握した内容を回答してください。選択肢の並べ変えや、意味の理解など、多様な質問が想定されるので質問を注意深くみてください。</s><s>### 指示\n{input_text}\n\n\n### 回答\n"

    # トークナイズ（token_type_idsを削除）
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    inputs.pop("token_type_ids", None)  # 不要なキーを削除

    # 推論
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        use_cache=True,
        do_sample=False,
        repetition_penalty=1.2,
    )
    # 結果のデコード
    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).split('\n### 回答')[-1]

    # 結果を保存
    results.append({"task_id": dt["task_id"], "input": input_text, "output": prediction})

# 推論結果の保存
output_file = f"{model_name.replace('/', '_')}_output.jsonl"
with open(output_file, "w") as f:
    for result in results:
        f.write(json.dumps(result, ensure_ascii=False) + "\n")

print(f"推論が完了しました。結果は {output_file} に保存されました。")

Training Details

Training Data

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  • —本モデルは、CC-BY-NC-SAライセンス下で提供されているデータセットを用いて学習されています。 このライセンスは、非営利的利用及び同一条件での共有を求めるため、利用者はライセンス条件を必ず確認してください。 参照: CC-BY-NC-SA ライセンス詳細

Training Procedure

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Preprocessing [optional]

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Training Hyperparameters
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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

  • —PEFT 0.13.2