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  library_name: transformers
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  tags:
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  - unsloth
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
 
 
 
 
 
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
 
 
 
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
 
 
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
 
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
 
 
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
 
 
 
 
 
 
 
 
 
 
 
 
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
 
 
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  library_name: transformers
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  tags:
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  - unsloth
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+ - japanese
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+ - llm-jp
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+ - lora
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+ datasets:
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+ - GENIAC-Team-Ozaki/Hachi-Alpaca_newans
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+ - llm-jp/magpie-sft-v1.0
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+ language:
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+ - ja
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+ base_model:
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+ - llm-jp/llm-jp-3-13b
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  ---
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+ # llm-jp-3-13b-SFT-LoRA モデルカード
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+ llm-jp-3-13bをベースに、QLoRAとUnslothを用いて効率的なファインチューニングを行った日本語言語モデルです。
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+ ## モデルの詳細
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+ ### モデルの説明
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+ - **開発者:** GENIAC Team
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+ - **共有者:** GENIAC Team
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+ - **モデルタイプ:** 言語モデル(デコーダーのみ)
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+ - **言語:** 日本語
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+ - **ライセンス:** ベースモデルに準拠
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+ - **ベースモデル:** llm-jp/llm-jp-3-13b
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+ ### モデルソース
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+ - **リポジトリ:** https://huggingface.co/llm-jp/llm-jp-3-13b
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+ ## 使用方法
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+ ### 直接利用
 
 
 
 
 
 
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+ このモデルは以下のような用途に適しています:
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+ - 質問応答
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+ - テキスト生成
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+ - 文章要約
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+ - その他の自然言語処理タスク
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+ ### 対象外の使用
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+ 以下の用途での使用は推奨されません:
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+ - 商用利用
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+ - 重要な意思決定
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+ - 医療・法律アドバイス
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+ - 有害なコンテンツの生成
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+ ## バイアス、リスク、制限事項
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+ - 学習データに起因するバイアスが存在する可能性があります
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+ - 事実と異なる情報を生成する可能性があります
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+ - 有害なコンテンツを生成する可能性があります
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+ ### 推奨事項
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+ - 出力内容の検証を必ず行ってください
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+ - センシティブな用途での使用は避けてください
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+ - 生成された内容の責任は使用者が負うものとします
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+ ## モデルの使用開始方法
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+ ## 学習の詳細
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+ ### 学習データ
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+ 以下のデータセットを使用:
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+ - GENIAC-Team-Ozaki/Hachi-Alpaca_newans
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+ - llm-jp/magpie-sft-v1.0
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+ ### 学習手順
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+ #### 前処理
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+ - 指示文と回答のペアにフォーマット
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+ - コンテキスト長を512トークンに制限
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+ #### 学習ハイパーパラメータ
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+ - **学習手法:** QLoRA with Unsloth
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+ - **量子化:** 4-bit
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+ - **LoRA設定:**
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+ - rank (r): 32
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+ - alpha: 32
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+ - dropout: 0.05
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+ - target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
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+ - **トレーニング設定:**
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+ - バッチサイズ: 2
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+ - 勾配累積: 4
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+ - エポック数: 1
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+ - 学習率: 2e-4
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+ - シーケンス長: 512
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+ ## 技術仕様
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+ ### 計算インフラ
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+ #### ハードウェア要件
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+ - CUDA対応GPU
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+ - 最小8GB VRAM推奨
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+ #### ソフトウェア要件
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+ - Python 3.10以上
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+ - PyTorch 2.0以上
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+ - Transformers最新版
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+ - Unsloth(推奨)