> For the complete documentation index, see [llms.txt](https://unsloth.ai/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://unsloth.ai/docs/jp/meru/unsloth-notebooks.md).

# Unslothノートブック

ファインチューニング用ノートブック: Unslothカタログを探索しましょう。

無料のGPUコンピュートを活用した、当社のノートブックで独自モデルを学習できます。［Run all］をクリック（またはローカルに保存）し、データセットを追加して、学習し、デプロイしてください。ノートブック内の任意のモデルを使用できます。

<a href="/docs/jp/meru/unsloth-notebooks.md#grpo-reasoning-rl" class="button secondary">GRPO（RL）</a><a href="/docs/jp/meru/unsloth-notebooks.md#text-to-speech-tts" class="button secondary">音声合成</a><a href="/docs/jp/meru/unsloth-notebooks.md#vision-multimodal" class="button secondary">ビジョン</a><a href="/docs/jp/meru/unsloth-notebooks.md#embedding-models" class="button secondary">埋め込み</a><a href="/docs/jp/meru/unsloth-notebooks.md#kaggle-notebooks" class="button secondary">Kaggle</a>

ノートブックについては、GitHubリポジトリもご覧ください: [github.com/unslothai/notebooks](https://github.com/unslothai/notebooks/)

## Colabノートブック

**ご紹介するのは** [**Unsloth Studio**](/docs/jp/shii/studio.md)✨ **ノートブック。** 22Bパラメータ以下のモデルを学習・実行:

{% embed url="<https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb>" %}

### 標準SFTノートブック:

* [**Qwen3.8 27B**](/docs/jp/moderu/qwen3.8/train.md): [会話（Kaggle）](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen3.8_\(27B\)-Conversational.ipynb\&accelerator=nvidiaTeslaT4)
* [**Muse Glimmer（30B）**](/docs/jp/moderu/muse-glimmer/train.md) **•** [会話（Kaggle）](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Muse_Glimmer_\(30B\)-Conversational.ipynb\&accelerator=nvidiaTeslaT4)  • [ビジョン（Kaggle）](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Muse_Glimmer_\(30B\)-Vision.ipynb\&accelerator=nvidiaTeslaT4)
* [**Gemma 4**](/docs/jp/moderu/gemma-4/train.md)**:** [E4B **（ビジョン）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(E4B\)-Vision.ipynb) **•** [E2B **（テキスト）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(E2B\)-Text.ipynb) **•** [E2B **（Audio）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(E2B\)-Audio.ipynb) **•** [**31B** （Kaggle）](https://www.kaggle.com/code/danielhanchen/gemma4-31b-unsloth) **•** [**推論**](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)
* [**Qwen3.5**](/docs/jp/moderu/qwen3.5/fine-tune.md)**:** [**0.8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(0_8B\)_Vision.ipynb) • [**2B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(2B\)_Vision.ipynb) • [**4B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(4B\)_Vision.ipynb)
* [gpt-oss（20b）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb) • [推論](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GPT_OSS_MXFP4_\(20B\)-Inference.ipynb) • [微調整](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-Fine-tuning.ipynb)
* [EmbeddingGemma（300M）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_\(300M\).ipynb) • [EmbeddingGemma-2 画像埋め込み](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma2_\(300M\)-Image_Text.ipynb)
* [Qwen3（14B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(14B\)-Reasoning-Conversational.ipynb) • [**Qwen3-VL（8B）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb)
* [**Qwen3-2507-4B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Instruct.ipynb) • [思考](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Thinking.ipynb) • [インストラクト](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-Instruct.ipynb)
* [Gemma 3（4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [テキスト](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\).ipynb) • [ビジョン](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb) • [270M](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(270M\).ipynb) • [**FunctionGemma**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\).ipynb)
* [Gemma 3n（E4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) • [テキスト](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Conversational.ipynb) • [ビジョン](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Vision.ipynb) • [音声](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb)
* [**Mistral Ministral 3**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_\(3B\)_Vision.ipynb)
* [**DeepSeek-OCR 2**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_2_\(3B\).ipynb)&#x20;
* [IBM Granite-4.0-H](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb)
* [Phi-4（14B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)
* [Llama 3.1（8B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Alpaca.ipynb) • [Llama 3.2（1B + 3B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)

### 意思決定モデル

* [**Qwen3.5 4B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(4B\)-Decision.ipynb) - 新規 - 型付き意思決定

### GRPO（推論RL）:

* [**Muse Glimmer（30B）**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Muse_Glimmer_\(30B\)-GRPO.ipynb\&accelerator=nvidiaTeslaT4)
* [**Gemma 4 E2B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(E2B\)_Reinforcement_Learning_Sudoku_Game.ipynb) - 新規
* [**Qwen3.5（4B）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(4B\)_Vision_GRPO.ipynb) - ビジョンGRPO
* [Llamaを使ったPPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_%281B%29-PPO.ipynb#scrollTo=zsiKGWFhYemJ) - 新しいPPOノートブック
* [gpt-oss-20b](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) （カーネルの自動生成）
* [Mistral Ministral 3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_\(3B\)_Reinforcement_Learning_Sudoku_Game.ipynb) （数独を解く）- 新規
* [Qwen3-8B - **FP8**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb) （L4）- 新規
* [Llama-3.2-1B - **FP8**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama_FP8_GRPO.ipynb) （L4）- 新規
* [gpt-oss-20b](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game.ipynb) （2048ゲームを自動で勝つ）&#x20;
* [Qwen3-VL（8B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - ビジョンGSPO
* [Qwen3（4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)-GRPO.ipynb) - 高度なGRPO LoRA
* [Gemma 3（4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - ビジョンGSPO
* [gpt-oss-20b](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/OpenEnv_gpt_oss_\(20B\)_Reinforcement_Learning_2048_Game.ipynb) （2048 OpenEnvの例）
* [DeepSeek-R1-0528-Qwen3（8B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DeepSeek_R1_0528_Qwen3_\(8B\)_GRPO.ipynb) （多言語ユースケース向け）
* [Gemma 3（1B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(1B\)-GRPO.ipynb)
* [Llama 3.2（3B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_2_\(3B\)_GRPO_LoRA.ipynb) - 高度なGRPO LoRA
* [Llama 3.1（8B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-GRPO.ipynb)
* [Phi-4（14B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_\(14B\)-GRPO.ipynb)
* [Mistral v0.3（7B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-GRPO.ipynb)
* [NeMo Gym マルチエージェント環境 ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/NeMo-Gym-Multi-Environment.ipynb)（複数のエージェント環境）

### テキスト読み上げ（TTS）:

* [Sesame-CSM（1B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Sesame_CSM_\(1B\)-TTS.ipynb)
* [Orpheus-TTS（3B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_\(3B\)-TTS.ipynb)
* [Whisper Large V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb) - 音声認識（STT）
* [Llasa-TTS（1B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llasa_TTS_\(1B\).ipynb)
* [Spark-TTS（0.5B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Spark_TTS_\(0_5B\).ipynb)
* [Oute-TTS（1B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Oute_TTS_\(1B\).ipynb)

**音声認識（SST）:**

* [**Gemma 4（E2B）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(E2B\)-Audio.ipynb) **- 音声 - 新規**
* [Whisper-Large-V3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Whisper.ipynb)
* [Gemma 3n（E4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Audio.ipynb) - 音声

### ビジョン（マルチモーダル）:

* [**Gemma 4**](/docs/jp/moderu/gemma-4/train.md)**:** [E2B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(E2B\)-Vision.ipynb) **•** [**31B** （Kaggle）](https://www.kaggle.com/code/danielhanchen/gemma4-31b-unsloth) - 新規
* [**Qwen3.5**](/docs/jp/moderu/qwen3.5/fine-tune.md)**:** [**0.8B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(0_8B\)_Vision.ipynb) • [**2B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(2B\)_Vision.ipynb) • [**4B**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_\(4B\)_Vision.ipynb) - 新規
* [**Qwen3-VL（8B）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision.ipynb)
* [**Mistral Ministral 3**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_\(3B\)_Vision.ipynb)
* [**DeepSeek-OCR**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_\(3B\).ipynb)
* [**Paddle-OCR（1B）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Paddle_OCR_\(1B\)_Vision.ipynb)
* [Gemma 3n（E4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_\(4B\)-Vision.ipynb)
* [Gemma 3（4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision.ipynb)
* [Llama 3.2 Vision（11B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(11B\)-Vision.ipynb)
* [Qwen2.5-VL（7B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_VL_\(7B\)-Vision.ipynb)
* [Pixtral（12B）2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_\(12B\)-Vision.ipynb)
* [Qwen3-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_\(8B\)-Vision-GRPO.ipynb) - ビジョンGSPO - 新規
* [Qwen2.5-VL](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb) - ビジョンGSPO
* [Gemma 3（4B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(4B\)-Vision-GRPO.ipynb) - ビジョンGSPO

### 埋め込みモデル:

* [EmbeddingGemma（300M）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_\(300M\).ipynb) - 新規
* **EmbeddingGemma2** - 新規 - [マルチモーダル検索](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma2_\(300M\)-Multimodal_Search.ipynb) • [画像](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma2_\(300M\)-Image_Text.ipynb) • [テキスト](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma2_\(300M\).ipynb) • [音声](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma2_\(300M\)-Audio.ipynb)
* [Qwen3-Embedding 4B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_Embedding_\(4B\).ipynb) - 新規
* [Qwen3-Embedding 0.6B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_Embedding_\(0_6B\).ipynb) - 新規
* [BGE M3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/BGE_M3.ipynb) - 新規
* [ModernBERT-large](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/bert_classification.ipynb) - 新規
* [All-MiniLM-L6-v2](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/All_MiniLM_L6_v2.ipynb) - 新規
* [GTE ModernBert](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/ModernBert.ipynb) - 新規

### 大規模LLM:

**大規模モデル向けノートブック:** これらはColabの無料15GB VRAM枠を超えます。Colabの新しい80GB GPUを使えば、120Bパラメータのモデルを微調整できます。

{% hint style="info" %}
Colabのサブスクリプションまたはクレジットが必要です。私たちは **しません** これらのノートブックから何も利益を得ていません。
{% endhint %}

* [**Gemma-4-31B**](https://www.kaggle.com/code/danielhanchen/gemma4-31b-unsloth) - 新規で **無料**
* [**DiffusionGemma**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/DiffusionGemma_\(26B-A4B\)-Sudoku.ipynb) - 新規
* [Gemma-4-26B-A4B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(26B_A4B\)-Vision.ipynb) - 新規
* [Gemma-4-31B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma4_\(31B\)-Vision.ipynb) - 新規
* [Qwen3.5-35B-A3B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_MoE.ipynb)
* [Qwen3.5‑27B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen_3_5_27B_A100\(80GB\).ipynb)
* [GLM-4.7-Flash](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/GLM_Flash_A100\(80GB\).ipynb)
* [gpt-oss-20b（50万コンテキスト）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_500K_Context_Fine_tuning.ipynb)
* [Qwen3-30B-A3B](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_MoE.ipynb)
* [Nemotron-3-Nano-30B-A3B LoRAノートブック](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Nemotron-3-Nano-30B-A3B_A100.ipynb)&#x20;
* [NeMo Gym 数独GRPOノートブック](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/NeMo-Gym-Sudoku.ipynb)
* [NeMo Gym マルチ環境GRPOノートブック](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/NeMo-Gym-Multi-Environment.ipynb)
* [gpt-oss-120b](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(120B\)_A100-Fine-tuning.ipynb)
* [Qwen3（32B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(32B\)_A100-Reasoning-Conversational.ipynb)
* [Llama 3.3（70B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.3_\(70B\)_A100-Conversational.ipynb)
* [Gemma 3（27B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_\(27B\)_A100-Conversational.ipynb)&#x20;
* [Baidu ERNIE 4.5 VL（28B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/ERNIE_4_5_VL_28B_A3B_PT_Vision.ipynb) - 新規

### その他の重要なノートブック:

* [**カスタマーサポートエージェント**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb)
* [Mistral Ministral 3](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_\(3B\)_Reinforcement_Learning_Sudoku_Game.ipynb) - 新規（数独を解く）
* [LM Studioにデプロイ ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\)-LMStudio.ipynb)- 新規
* [量子化対応学習](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb) （QAT）- 新規
* [スマホへのデプロイ ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(0_6B\)-Phone_Deployment.ipynb)- 新規
* [先に推論 **ツール呼び出し** ノートブック](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\).ipynb) - 新規
* [モバイルアクションノートブック](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\)-Mobile-Actions.ipynb) - 新規
* [**自動カーネル作成**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) RL付き
* [**ModernBERT-large**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/bert_classification.ipynb) **- 新規** 8月19日
* [**合成データ生成 Llama 3.2（3B）**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_\(3B\).ipynb)
* [gpt-oss-20b（50万コンテキスト）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt_oss_\(20B\)_500K_Context_Fine_tuning.ipynb) - 新規（A100）
* [**ツール呼び出し**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb)
* [Mistral v0.3 Instruct（7B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Conversational.ipynb)
* [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
* [ORPO](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-ORPO.ipynb)
* [継続事前学習](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb)
* [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
* [***推論のみ***](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_\(8B\)-Inference.ipynb)
* [Llama 3（8B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Alpaca.ipynb)

### 特定のユースケース向けノートブック:

* [スマホへのデプロイ ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(0_6B\)-Phone_Deployment.ipynb)- 新規
* [LM Studioにデプロイ ](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\)-LMStudio.ipynb)- 新規
* [先に推論 **ツール呼び出し**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\).ipynb) - 新規
* [モバイルアクション](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/FunctionGemma_\(270M\)-Mobile-Actions.ipynb) - 新規
* [**カスタマーサポートエージェント**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb)
* [量子化対応学習](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_\(4B\)_Instruct-QAT.ipynb) （QAT）- 新規
* [**自動カーネル作成**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-\(20B\)-GRPO.ipynb) RL付き **- 新規**
* [DPO Zephyr](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_\(7B\)-DPO.ipynb)
* [BERT - テキスト分類](https://colab.research.google.com/github/timothelaborie/text_classification_scripts/blob/main/unsloth_classification.ipynb) -（AutoModelForSequenceClassification）
* [Ollama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_\(8B\)-Ollama.ipynb)
* [**ツール呼び出し**](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb)
* [継続事前学習（CPT）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-CPT.ipynb)
* [複数データセット](https://colab.research.google.com/drive/1njCCbE1YVal9xC83hjdo2hiGItpY_D6t?usp=sharing) Flailによる
* [KTO](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) Jeffreyによる
* [推論チャットUI](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb)
* [会話](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
* [ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
* [テキスト補完](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_\(7B\)-Text_Completion.ipynb)

### その他のノートブック:

* [Qwen2.5（3B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_\(3B\)-GRPO.ipynb)
* [Gemma 2（9B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(9B\)-Alpaca.ipynb)
* [Mistral NeMo（12B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Nemo_\(12B\)-Alpaca.ipynb)
* [Phi-3.5（mini）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3.5_Mini-Conversational.ipynb)
* [Phi-3（medium）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_3_Medium-Conversational.ipynb)
* [Gemma 2（2B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma2_\(2B\)-Alpaca.ipynb)
* [Qwen 2.5 Coder（14B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_\(14B\)-Conversational.ipynb)
* [Mistral Small（22B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_\(22B\)-Alpaca.ipynb)
* [TinyLlama](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/TinyLlama_\(1.1B\)-Alpaca.ipynb)
* [CodeGemma（7B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/CodeGemma_\(7B\)-Conversational.ipynb)
* [Mistral v0.3（7B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_\(7B\)-Alpaca.ipynb)
* [Qwen2（7B）](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_\(7B\)-Alpaca.ipynb)

## Kaggleノートブック

#### 標準ノートブック:

* [**Gemma-4-31B** （Kaggle）](https://www.kaggle.com/code/danielhanchen/gemma4-31b-unsloth) - 新規で **無料**
* [**gpt-oss（20B）**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-gpt-oss-\(20B\)-Fine-tuning.ipynb\&accelerator=nvidiaTeslaT4)
* [Gemma 3n（E4B）](https://www.kaggle.com/code/danielhanchen/gemma-3n-4b-multimodal-finetuning-inference)
* [Qwen3（14B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(14B\).ipynb)
* [Magistral-2509（24B）](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Magistral_\(24B\)-Reasoning-Conversational.ipynb\&accelerator=nvidiaTeslaT4)
* [Gemma 3（4B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(4B\).ipynb)
* [Phi-4（14B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4-Conversational.ipynb)
* [Llama 3.1（8B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Alpaca.ipynb)
* [Llama 3.2（1B + 3B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(1B_and_3B\)-Conversational.ipynb)
* [Qwen 2.5（7B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(7B\)-Alpaca.ipynb)

#### GRPO（推論）ノートブック:

* [**Qwen2.5-VL**](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2_5_7B_VL_GRPO.ipynb\&accelerator=nvidiaTeslaT4) - ビジョンGRPO - 新規
* [Qwen3（4B）](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen3_\(4B\)-GRPO.ipynb\&accelerator=nvidiaTeslaT4)
* [Gemma 3（1B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma3_\(1B\)-GRPO.ipynb)
* [Llama 3.1（8B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-GRPO.ipynb)
* [Phi-4（14B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Phi_4_\(14B\)-GRPO.ipynb)
* [Qwen 2.5（3B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_\(3B\)-GRPO.ipynb)

#### テキスト読み上げ（TTS）ノートブック:

* [Sesame-CSM（1B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Sesame_CSM_\(1B\)-TTS.ipynb)
* [Orpheus-TTS（3B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Orpheus_\(3B\)-TTS.ipynb)
* [Whisper Large V3](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Whisper.ipynb) – 音声認識
* [Llasa-TTS（1B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llasa_TTS_\(1B\).ipynb)
* [Spark-TTS（0.5B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Spark_TTS_\(0_5B\).ipynb)
* [Oute-TTS（1B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Oute_TTS_\(1B\).ipynb)

#### ビジョン（マルチモーダル）ノートブック:

* [Llama 3.2 Vision（11B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.2_\(11B\)-Vision.ipynb)
* [Qwen 2.5-VL（7B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_VL_\(7B\)-Vision.ipynb)
* [Pixtral（12B）2409](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Pixtral_\(12B\)-Vision.ipynb)

#### 特定のユースケース向けノートブック:

* [ツール呼び出し](https://www.kaggle.com/notebooks/welcome?src=https://github.com/unslothai/notebooks/blob/main/nb/Kaggle-Qwen2.5_Coder_\(1.5B\)-Tool_Calling.ipynb\&accelerator=nvidiaTeslaT4)
* [ORPO](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-ORPO.ipynb)
* [継続事前学習](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_v0.3_\(7B\)-CPT.ipynb)
* [DPO Zephyr](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Zephyr_\(7B\)-DPO.ipynb)
* [推論のみ](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3.1_\(8B\)-Inference.ipynb)
* [Ollama](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Llama3_\(8B\)-Ollama.ipynb)
* [テキスト補完](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_\(7B\)-Text_Completion.ipynb)
* [CodeForces-cot（推論）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeForces-cot-Finetune_for_Reasoning_on_CodeForces.ipynb)
* [Unsloth Studio（チャットUI）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Unsloth_Studio.ipynb)

#### その他のノートブック:

* [Gemma 2（9B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(9B\)-Alpaca.ipynb)
* [Gemma 2（2B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Gemma2_\(2B\)-Alpaca.ipynb)
* [CodeGemma（7B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-CodeGemma_\(7B\)-Conversational.ipynb)
* [Mistral NeMo（12B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Nemo_\(12B\)-Alpaca.ipynb)
* [Mistral Small（22B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-Mistral_Small_\(22B\)-Alpaca.ipynb)
* [TinyLlama（1.1B）](https://www.kaggle.com/notebooks/welcome?src=https%3A%2F%2Fgithub.com%2Funslothai/notebooks/blob/main/nb/Kaggle-TinyLlama_\(1.1B\)-Alpaca.ipynb)

Kaggleノートブックの完全な一覧を表示するには、 [こちらをクリック](https://github.com/unslothai/notebooks#-kaggle-notebooks).

{% hint style="info" %}
ぜひノートブックにご協力ください。私たちの [リポジトリ](https://github.com/unslothai/notebooks)!
{% endhint %}


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://unsloth.ai/docs/jp/meru/unsloth-notebooks.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.