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README.md
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README.md
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## ✨ Train for Free
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Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-guide). Add dataset, run, then export your trained model to GGUF, Ollama, vLLM or Hugging Face.
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Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-guide). Add dataset, run, then export your trained model to GGUF, llama.cpp, Ollama, vLLM, SGLang or Hugging Face.
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| Model | Free Notebooks | Performance | Memory use |
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|-----------|---------|--------|----------|
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| **gpt-oss (20B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb) | 1.5x faster | 70% less |
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| **Qwen3 (14B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(14B)-Reasoning-Conversational.ipynb) | 2x faster | 70% less |
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| **DeepSeek-OCR (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb) | 1.5x faster | 30% less |
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| **gpt-oss (20B): GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) | 2x faster | 80% less |
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| **Qwen3-VL (8B): GSPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-GRPO.ipynb) | 1.5x faster | 80% less |
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| **Qwen3-VL (8B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision.ipynb) | 2x faster | 50% less |
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| **Gemma 3 (270M)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(270M).ipynb) | 1.7x faster | 60% less |
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| **Gemma 3n (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3N_(4B)-Conversational.ipynb) | 1.5x faster | 50% less |
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| **DeepSeek-OCR (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb) | 1.5x faster | 30% less |
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| **Llama 3.1 (8B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2x faster | 70% less |
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| **Orpheus-TTS (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Orpheus_(3B)-TTS.ipynb) | 1.5x faster | 50% less |
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@ -90,7 +90,7 @@ For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](
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| ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog)|
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## ⭐ Key Features
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- Supports **full-finetuning**, pretraining, 4b-bit, 16-bit and **8-bit** training
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- Supports **full-finetuning**, pretraining, 4b-bit, 16-bit and **FP8** training
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- Supports **all models** including [TTS](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning), multimodal, [BERT](https://docs.unsloth.ai/get-started/unsloth-notebooks#other-important-notebooks) and more! Any model that works in transformers, works in Unsloth.
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- The most efficient library for [Reinforcement Learning (RL)](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc.
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- **0% loss in accuracy** - no approximation methods - all exact.
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@ -347,7 +347,7 @@ trainer = SFTTrainer(
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trainer.train()
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# Go to https://docs.unsloth.ai for advanced tips like
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# (1) Saving to GGUF / merging to 16bit for vLLM
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# (1) Saving to GGUF / merging to 16bit for vLLM or SGLang
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# (2) Continued training from a saved LoRA adapter
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# (3) Adding an evaluation loop / OOMs
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# (4) Customized chat templates
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<a name="RL"></a>
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## 💡 Reinforcement Learning
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[RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) including [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth.
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[RL](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) including [GRPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), **FP8** traning, DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth.
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Read our [Reinforcement Learning Guide](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide) or our [advanced RL docs](https://docs.unsloth.ai/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation) for batching, generation & training parameters.
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List of RL notebooks:
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- gpt-oss GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb)
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- Qwen2.5-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_5_7B_VL_GRPO.ipynb)
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- Advanced Qwen3 GRPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb)
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- ***FP8*** Qwen3-8B GRPO notebook (L4): [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb)
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- ORPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-ORPO.ipynb)
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- DPO Zephyr notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb)
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- KTO notebook: [Link](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing)
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