Fix broken documentation links, typos, and formatting in README (#4003)
- Fix 14 broken documentation links (all returning 404) caused by docs site restructuring (install-and-update -> install, pages moved to /docs/blog/ and /docs/models/tutorials/) - Fix "Qwen2.3-VL" -> "Qwen3-VL" (model does not exist) - Fix incorrect "GSPO" label on gpt-oss GRPO notebook - Fix "4b-bit" typo -> "4-bit" - Fix "sodoku" typo -> "sudoku" - Fix double dash formatting on FP8 GRPO notebook list item - Fix citation URL from http:// to https:// - Update "MultiGPU coming soon" to "is now supported" - Fix Windows installation step numbering (1,3,5,6,7 -> 1,2,3,4,5) - Fix Advanced/Troubleshooting step numbering (5,6,5 -> 4,5,6) Co-authored-by: Daniel Hanchen <danielhanchen@users.noreply.github.com>
This commit is contained in:
Daniel Han
GitHub
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README.md
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README.md
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@ -44,36 +44,36 @@ Notebooks are beginner friendly. Read our [guide](https://unsloth.ai/docs/get-st
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pip install unsloth
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```
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### Windows
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For Windows, `pip install unsloth` works only if you have Pytorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install-and-update/windows-installation).
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For Windows, `pip install unsloth` works only if you have Pytorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install/windows-installation).
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### Docker
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Use our official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Read our [Docker Guide](https://unsloth.ai/docs/get-started/install-and-update/docker).
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Use our official [Unsloth Docker image](https://hub.docker.com/r/unsloth/unsloth) ```unsloth/unsloth``` container. Read our [Docker Guide](https://unsloth.ai/docs/get-started/install/docker).
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### Blackwell & DGX Spark
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For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark Guide](https://unsloth.ai/docs/basics/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth) for more details.
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For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark Guide](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth) for more details.
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## 🦥 Unsloth News
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- **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. [Blog](https://unsloth.ai/docs/new/embedding-finetuning) • [Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)
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- New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context)
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- New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing)
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- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/new/500k-context-length-fine-tuning)
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- **FP8 Reinforcement Learning**: You can now do FP8 GRPO on consumer GPUs. [Blog](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb)
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- **DeepSeek-OCR**: Fine-tune to improve language understanding by 89%. [Guide](https://unsloth.ai/docs/models/deepseek-ocr-how-to-run-and-fine-tune) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb)
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- **Docker**: Use Unsloth with no setup & environment issues with our new image. [Guide](https://unsloth.ai/docs/new/how-to-fine-tune-llms-with-unsloth-and-docker) • [Docker image](https://hub.docker.com/r/unsloth/unsloth)
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- **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU. [Blog](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)
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- **FP8 Reinforcement Learning**: You can now do FP8 GRPO on consumer GPUs. [Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb)
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- **DeepSeek-OCR**: Fine-tune to improve language understanding by 89%. [Guide](https://unsloth.ai/docs/models/tutorials/deepseek-ocr-how-to-run-and-fine-tune) • [Notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Deepseek_OCR_(3B).ipynb)
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- **Docker**: Use Unsloth with no setup & environment issues with our new image. [Guide](https://unsloth.ai/docs/blog/how-to-fine-tune-llms-with-unsloth-and-docker) • [Docker image](https://hub.docker.com/r/unsloth/unsloth)
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- **Vision RL**: You can now train VLMs with GRPO or GSPO in Unsloth! [Read guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)
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- **gpt-oss** by OpenAI: Read our [RL blog](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning), [Flex Attention](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training) blog and [gpt-oss Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune). 20B works on 14GB VRAM. 120B on 65GB.
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<details>
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<summary>Click for more news</summary>
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- **Quantization-Aware Training**: We collabed with Pytorch, recovering ~70% accuracy. [Read blog](https://unsloth.ai/docs/basics/quantization-aware-training-qat)
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- **Quantization-Aware Training**: We collabed with Pytorch, recovering ~70% accuracy. [Read blog](https://unsloth.ai/docs/blog/quantization-aware-training-qat)
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- **Memory-efficient RL**: We're introducing even better RL. Our new kernels & algos allows faster RL with 50% less VRAM & 10× more context. [Read blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/memory-efficient-rl)
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- **Mistral 3**: Run Ministral 3 or Devstral 2 and fine-tune with vision/RL sodoku notebooks. [Guide](https://unsloth.ai/docs/models/ministral-3) • [Notebooks](https://unsloth.ai/docs/models/ministral-3#fine-tuning-ministral-3)
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- **Mistral 3**: Run Ministral 3 or Devstral 2 and fine-tune with vision/RL sudoku notebooks. [Guide](https://unsloth.ai/docs/models/tutorials/ministral-3) • [Notebooks](https://unsloth.ai/docs/models/ministral-3#fine-tuning-ministral-3)
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- **Gemma 3n** by Google: [Read Blog](https://unsloth.ai/docs/models/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3n-685d3874830e49e1c93f9339).
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- **[Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning)** is now supported, including `sesame/csm-1b` and STT `openai/whisper-large-v3`.
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- **[Qwen3](https://unsloth.ai/docs/models/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM.
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- Introducing **[Dynamic 2.0](https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs)** quants that set new benchmarks on 5-shot MMLU & Aider Polyglot.
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- [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) - all models (TTS, BERT, Mamba), FFT, etc. [MultiGPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) coming soon. Enable FFT with `full_finetuning = True`, 8-bit with `load_in_8bit = True`.
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- [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) - all models (TTS, BERT, Mamba), FFT, etc. [MultiGPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) is now supported. Enable FFT with `full_finetuning = True`, 8-bit with `load_in_8bit = True`.
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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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- 📣 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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- 📣 Introducing Unsloth [Dynamic 4-bit Quantization](https://unsloth.ai/blog/dynamic-4bit)! We dynamically opt not to quantize certain parameters and this greatly increases accuracy while only using <10% more VRAM than BnB 4-bit. See our collection on [Hugging Face here.](https://huggingface.co/collections/unsloth/unsloth-4-bit-dynamic-quants-67503bb873f89e15276c44e7)
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@ -92,24 +92,24 @@ For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our [Blackwell Guide](
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| <img width="15" src="https://redditinc.com/hs-fs/hubfs/Reddit%20Inc/Brand/Reddit_Logo.png" /> **r/unsloth Reddit** | [Join Reddit community](https://reddit.com/r/unsloth) |
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| 📚 **Documentation & Wiki** | [Read Our Docs](https://unsloth.ai/docs) |
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| <img width="13" src="https://upload.wikimedia.org/wikipedia/commons/0/09/X_(formerly_Twitter)_logo_late_2025.svg" /> **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai) |
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| 💾 **Installation** | [Pip & Docker Install](https://unsloth.ai/docs/get-started/install-and-update) |
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| 💾 **Installation** | [Pip & Docker Install](https://unsloth.ai/docs/get-started/install) |
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| 🔮 **Our Models** | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog) |
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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 **FP8** training
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* Supports **full-finetuning**, pretraining, 4-bit, 16-bit and **FP8** training
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* Supports **all models** including [TTS](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), multimodal, [embedding](https://unsloth.ai/docs/new/embedding-finetuning) and more! Any model that works in transformers, works in Unsloth.
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* The most efficient library for [Reinforcement Learning (RL)](https://unsloth.ai/docs/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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* Export and [deploy your model](https://unsloth.ai/docs/basics/inference-and-deployment) to GGUF, llama.cpp, vLLM, SGLang and Hugging Face.
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* Supports NVIDIA (since 2018), [AMD](https://unsloth.ai/docs/get-started/install-and-update/amd) and Intel GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc)
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* Supports NVIDIA (since 2018), [AMD](https://unsloth.ai/docs/get-started/install/amd) and Intel GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc)
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* Works on **Linux**, WSL and **Windows**
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* All kernels written in OpenAI's Triton language. Manual backprop engine.
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* If you trained a model with 🦥Unsloth, you can use this cool sticker! <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200" align="center" />
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## 💾 Install Unsloth
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You can also see our docs for more detailed installation and updating instructions [here](https://unsloth.ai/docs/get-started/install-and-update).
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You can also see our docs for more detailed installation and updating instructions [here](https://unsloth.ai/docs/get-started/install).
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Unsloth supports Python 3.13 or lower.
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@ -128,17 +128,17 @@ See [here](#advanced-pip-installation) for advanced pip install instructions.
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1. **Install NVIDIA Video Driver:**
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You should install the latest driver for your GPU. Download drivers here: [NVIDIA GPU Driver](https://www.nvidia.com/Download/index.aspx).
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3. **Install Visual Studio C++:**
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You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For detailed instructions with options, see [here](https://unsloth.ai/docs/get-started/install-and-update/windows-installation#method-3-windows-directly).
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2. **Install Visual Studio C++:**
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You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. For detailed instructions with options, see [here](https://unsloth.ai/docs/get-started/install/windows-installation#method-3-windows-directly).
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5. **Install CUDA Toolkit:**
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3. **Install CUDA Toolkit:**
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Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).
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6. **Install PyTorch:**
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4. **Install PyTorch:**
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You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully.
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[Install PyTorch](https://pytorch.org/get-started/locally/).
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7. **Install Unsloth:**
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5. **Install Unsloth:**
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```python
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pip install unsloth
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@ -163,9 +163,9 @@ pip install unsloth
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```
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Check if `xformers` succeeded with `python -m xformers.info` Go to https://github.com/facebookresearch/xformers. Another option is to install `flash-attn` for Ampere GPUs and ignore `xformers`
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5. For GRPO runs, you can try installing `vllm` and seeing if `pip install vllm` succeeds.
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6. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful.
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5. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
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4. For GRPO runs, you can try installing `vllm` and seeing if `pip install vllm` succeeds.
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5. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful.
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6. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes`
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### Conda Installation (Optional)
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`⚠️Only use Conda if you have it. If not, use Pip`. Select either `pytorch-cuda=11.8,12.1` for CUDA 11.8 or CUDA 12.1. We support `python=3.10,3.11,3.12`.
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```
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### Docker Installation
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You can use our pre-built Docker container with all dependencies to use Unsloth instantly with no setup required.
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[Read our guide](https://unsloth.ai/docs/get-started/install-and-update/docker).
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[Read our guide](https://unsloth.ai/docs/get-started/install/docker).
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This container requires installing [NVIDIA's Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).
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@ -285,7 +285,7 @@ Access Jupyter Lab at `http://localhost:8888` and start fine-tuning!
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## 📜 Documentation
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* Go to our official [Documentation](https://unsloth.ai/docs) for [running models](https://unsloth.ai/docs/basics/inference-and-deployment), [saving to GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf), [checkpointing](https://unsloth.ai/docs/basics/finetuning-from-last-checkpoint), [evaluation](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide#evaluation) and more!
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* Read our Guides for: [Fine-tuning](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide), [Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), [Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [Vision](https://unsloth.ai/docs/basics/vision-fine-tuning) and [any model](https://unsloth.ai/docs/models/tutorials-how-to-fine-tune-and-run-llms).
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* Read our Guides for: [Fine-tuning](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide), [Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide), [Text-to-Speech (TTS)](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [Vision](https://unsloth.ai/docs/basics/vision-fine-tuning) and [any model](https://unsloth.ai/docs/models/tutorials).
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* We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code.
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Unsloth example code to fine-tune gpt-oss-20b:
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<a name="RL"></a>
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## 💡 Reinforcement Learning
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[RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) including [GRPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), [**FP8** training](https://unsloth.ai/docs/new/fp8-reinforcement-learning), DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth.
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[RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) including [GRPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide#training-with-grpo), [GSPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/gspo-reinforcement-learning), [**FP8** training](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning), DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth.
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Read our [Reinforcement Learning Guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) or our [advanced RL docs](https://unsloth.ai/docs/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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- - ***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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- Qwen2.3-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-GRPO.ipynb)
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- gpt-oss GRPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-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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- Qwen3-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-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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- 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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@ -420,7 +420,7 @@ You can cite the Unsloth repo as follows:
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@software{unsloth,
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author = {Daniel Han, Michael Han and Unsloth team},
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title = {Unsloth},
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url = {http://github.com/unslothai/unsloth},
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url = {https://github.com/unslothai/unsloth},
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year = {2023}
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}
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```
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