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README.md
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@ -23,20 +23,18 @@ Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-st
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| Unsloth supports | Free Notebooks | Performance | Memory use |
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|-----------|---------|--------|----------|
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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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| **GRPO (reasoning)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb) | 2x faster | 80% less |
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| **Llama 3.2 (3B): GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Advanced_Llama3_1_(3B)_GRPO_LoRA.ipynb) | 2x faster | 80% less |
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| **Gemma 3 (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B).ipynb) | 1.6x faster | 60% less |
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| **Llama 3.2 (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb) | 2x faster | 70% less |
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| **Phi-4 (14B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb) | 2x faster | 70% less |
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| **Llama 3.2 Vision (11B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb) | 2x faster | 50% 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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| **Mistral v0.3 (7B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb) | 2.2x faster | 75% less |
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| **Ollama** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb) | 1.9x faster | 60% 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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- See [all our notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) and [all our models](https://docs.unsloth.ai/get-started/all-our-models)
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- **Kaggle Notebooks** for [Llama 3.2](https://www.kaggle.com/danielhanchen/kaggle-llama-3-2-1b-3b-unsloth-notebook), [Llama 3.1 (8B)](https://www.kaggle.com/danielhanchen/kaggle-llama-3-1-8b-unsloth-notebook), [Phi-4 (14B)](https://www.kaggle.com/code/danielhanchen/phi-4-finetuning-unsloth-notebook), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)
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- Don't have data? Use our [Synthetic Dataset notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_(3B).ipynb) in collaboration with Meta.
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- See detailed documentation for Unsloth [here](https://docs.unsloth.ai/).
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- See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://docs.unsloth.ai/get-started/unsloth-notebooks#grpo-reasoning-notebooks), **[TTS](https://docs.unsloth.ai/get-started/unsloth-notebooks#text-to-speech-tts-notebooks)** & [Vision](https://docs.unsloth.ai/get-started/unsloth-notebooks#vision-multimodal-notebooks)
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- See [all our models](https://docs.unsloth.ai/get-started/all-our-models) and our [Synthetic Dataset notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Meta_Synthetic_Data_Llama3_2_%283B%29.ipynb) in collaboration with Meta
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- See detailed documentation for Unsloth [here](https://docs.unsloth.ai/)
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## ⚡ Quickstart
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@ -47,14 +45,14 @@ pip install unsloth
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For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/installing-+-updating/windows-installation).
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## 🦥 Unsloth.ai News
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- 📣 NEW! **[Qwen3](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** is now supported! Qwen3-30B-A3B fits on 17.5GB VRAM.
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- 📣 NEW! **[Text-to-Speech (TTS)](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and `openai/whisper-large-v3`.
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- 📣 NEW! **[Qwen3](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM.
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- 📣 NEW! Introducing **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & KL Divergence.
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- 📣 **[Llama 4](https://unsloth.ai/blog/llama4)**, Meta's latest models including Scout & Maverick are now supported.
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- 📣 NEW! [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) including: FFT, ALL models (Mixtral, MOE, Cohere, Mamba) and all training algorithms (KTO, DoRA) etc. MultiGPU support coming very soon.
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To enable full-finetuning, set ```full_finetuning = True``` and for 8-bit finetuning, set ```load_in_8bit = True```
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- 📣 **[Llama 4](https://unsloth.ai/blog/llama4)** by Meta, including Scout & Maverick are now supported.
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- 📣 NEW! [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) including: FFT, all models (BERT, Cohere, Mamba) and training algorithms (KTO, DoRA). MultiGPU support coming very soon. To enable FFT, set ```full_finetuning = True``` and for 8-bit, set ```load_in_8bit = True```
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- 📣 **Gemma 3** by Google: [Read Blog](https://unsloth.ai/blog/gemma3). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b).
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- 📣 Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
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- 📣 [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - the most powerful open reasoning models with Llama & Qwen distillations. Run or fine-tune them now [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
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- 📣 [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - run or fine-tune them [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
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<details>
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<summary>Click for more news</summary>
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@ -64,12 +62,6 @@ For Windows install instructions, see [here](https://docs.unsloth.ai/get-started
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- 📣 [Llama 3.3 (70B)](https://huggingface.co/collections/unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f), Meta's latest model is supported.
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- 📣 We worked with Apple to add [Cut Cross Entropy](https://arxiv.org/abs/2411.09009). Unsloth now supports 89K context for Meta's Llama 3.3 (70B) on a 80GB GPU - 13x longer than HF+FA2. For Llama 3.1 (8B), Unsloth enables 342K context, surpassing its native 128K support.
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- 📣 We found and helped fix a [gradient accumulation bug](https://unsloth.ai/blog/gradient)! Please update Unsloth and transformers.
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- 📣 Try out [Chat interface](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb)!
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- 📣 NEW! Qwen-2.5 including [Coder](https://unsloth.ai/blog/qwen-coder) models are now supported with bugfixes. 14b fits in a Colab GPU! [Qwen 2.5 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_(14B)-Conversational.ipynb)
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- 📣 NEW! [Mistral Small 22b notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_(22B)-Alpaca.ipynb) finetuning fits in under 16GB of VRAM!
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- 📣 NEW! `pip install unsloth` now works! Head over to [pypi](https://pypi.org/project/unsloth/) to check it out! This allows non git pull installs. Use `pip install unsloth[colab-new]` for non dependency installs.
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- 📣 NEW! Continued Pretraining [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-CPT.ipynb) for other languages like Korean!
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- 📣 [2x faster inference](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Inference.ipynb) added for all our models
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- 📣 We cut memory usage by a [further 30%](https://unsloth.ai/blog/long-context) and now support [4x longer context windows](https://unsloth.ai/blog/long-context)!
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</details>
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@ -326,6 +318,7 @@ trainer.train()
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## 💡 Reinforcement Learning
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RL including DPO, GRPO, PPO, Reward Modelling, Online DPO all work with Unsloth. We're in 🤗Hugging Face's official docs! We're on the [GRPO docs](https://huggingface.co/learn/nlp-course/en/chapter12/6) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)! List of RL notebooks:
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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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- 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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@ -439,8 +432,8 @@ You can cite the Unsloth repo as follows:
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```
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### Thank You to
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- Hugging Face's [TRL library](https://github.com/huggingface/trl) which serves as the basis foundation for Unsloth
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- The [llama.cpp library](https://github.com/ggml-org/llama.cpp) that lets users save models with Unsloth
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- The Hugging Face team and their [TRL library](https://github.com/huggingface/trl)
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- [Erik](https://github.com/erikwijmans) for his help adding [Apple's ML Cross Entropy](https://github.com/apple/ml-cross-entropy) in Unsloth
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- [HuyNguyen-hust](https://github.com/HuyNguyen-hust) for making [RoPE Embeddings 28% faster](https://github.com/unslothai/unsloth/pull/238)
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- [RandomInternetPreson](https://github.com/RandomInternetPreson) for confirming WSL support
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- [152334H](https://github.com/152334H) for experimental DPO support
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- [Etherl](https://github.com/Etherll) for adding support for [TTS, diffusion and BERT models](https://github.com/unslothai/notebooks/pull/34)
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- And of course for every single person who has contributed or has used Unsloth!
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