Update README.md
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Michael Han
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README.md
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README.md
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@ -39,6 +39,14 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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- This [continued pretraining notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-CPT.ipynb) is for learning another language
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- Click [here](https://docs.unsloth.ai/) for detailed documentation for Unsloth.
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## ⚡ Quickstart
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- **Install with pip (recommended)** for Linux devices:
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```
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pip install unsloth
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```
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For Windows install instructions, see [here](https://github.com/unslothai/unsloth/edit/main/README.md#windows-installation).
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## 🦥 Unsloth.ai News
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- 📣 NEW! Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. You can now reproduce DeepSeek-R1's "aha" moment with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!
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- 📣 NEW! [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - the most powerful open reasoning models with Llama & Qwen distillations. Run or fine-tune them now! More details: [unsloth.ai/blog/deepseek-r1](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).
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@ -74,17 +82,63 @@ All notebooks are **beginner friendly**! Add your dataset, click "Run All", and
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- All kernels written in [OpenAI's Triton](https://openai.com/index/triton/) language. **Manual backprop engine**.
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- **0% loss in accuracy** - no approximation methods - all exact.
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- No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.
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- Works on **Linux** and **Windows** via WSL.
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- Works on **Linux** and **Windows**
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- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
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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" height="50" align="center" />
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## 💾 Install Unsloth
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You can also see our documentation for more detailed installation and updating instructions [here](https://docs.unsloth.ai/get-started/installing-+-updating).
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- **Install with pip (recommended)** for Linux devices:
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### Pip Installation
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**Install with pip (recommended) for Linux devices:**
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```
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pip install unsloth
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```
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See below for Windows install instructions:
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See [here](https://github.com/unslothai/unsloth/edit/main/README.md#advanced-pip-installation) for advanced pip install instructions.
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### Windows Installation
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> [!warning]
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> Python 3.13 does not support Unsloth. Use 3.12, 3.11 or 3.10
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1. **Install NVIDIA Video Driver:**:
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You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](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, so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK. [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/)
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5. **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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You will need the correct version of PyTorch that is compatibile 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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```python
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pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git"
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```
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#### Notes
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To run Unsloth directly on Windows:
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- Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)
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- In the SFTTrainer, set `dataset_num_proc=1` to avoid a crashing issue:
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```python
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trainer = SFTTrainer(
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dataset_num_proc=1,
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...
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)
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```
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#### Advanced/Troubleshooting
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For **advanced installation instructions** or if you see weird errors during installations:
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1. Install `torch` and `triton`. Go to https://pytorch.org to install it. For example `pip install torch torchvision torchaudio triton`
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2. Confirm if CUDA is installated correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
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3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. 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.
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4. 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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### 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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```bash
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@ -111,7 +165,7 @@ pip install unsloth
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```
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</details>
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### Pip Installation
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### Advanced Pip Installation
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`⚠️Do **NOT** use this if you have Conda.` Pip is a bit more complex since there are dependency issues. The pip command is different for `torch 2.2,2.3,2.4,2.5` and CUDA versions.
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For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240` and for CUDA versions, we support `cu118` and `cu121` and `cu124`. For Ampere devices (A100, H100, RTX3090) and above, use `cu118-ampere` or `cu121-ampere` or `cu124-ampere`.
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@ -168,71 +222,7 @@ x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
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print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"')
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```
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## Windows Installation
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> [!warning]
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> Python 3.13 does not support Unsloth. Use 3.12, 3.11 or 3.10
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### Step 1: NVIDIA Video Driver
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You should install the latest version of your GPUs driver. You can download drivers here:
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- [NVIDIA GPU Drive Download](https://www.nvidia.com/Download/index.aspx)
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### Step 2: Visual Studio C++
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You will need Visual Studio, with C++ installed. By default, C++ is not installed with Visual Studio, so make sure you select all of the C++ options. Also select options for Windows 10/11 SDK.
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- [Visual Studio Community Edition](https://visualstudio.microsoft.com/vs/community/)
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<table>
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<tr>
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<td>
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<img src="https://github.com/user-attachments/assets/d3e6ca95-85bb-442a-8c6f-81944300598e" alt="VSCode C++ Ref Image" width="400" height="350"/>
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</td>
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<td>
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<div align="center">
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<h1>Steps to configure VS C++</h1>
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</div>
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<ol>
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<li>Launch the Installer downloaded from the link above.</li>
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<li>In the installer, navigate to Individual components and select all the options mentioned in the image.</li>
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<li>Click on install now.</li>
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</ol>
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</td>
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</tr>
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</table>
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### Step 3: CUDA Toolkit
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- [Download CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive)
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### Step 4: Install PyTorch
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You will need the correct version of PyTorch that is compatibile 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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### Step 5: Install Unsloth
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```python
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pip install "unsloth[windows] @ git+https://github.com/unslothai/unsloth.git"
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```
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### Side note
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To run Unsloth directly on Windows:
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- Install Triton from this Windows fork and follow the instructions: https://github.com/woct0rdho/triton-windows (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)
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- In the SFTTrainer, set `dataset_num_proc=1` to avoid a crashing issue:
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```python
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trainer = SFTTrainer(
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dataset_num_proc=1,
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...
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)
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```
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### Advanced/Troubleshooting
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For **advanced installation instructions** or if you see weird errors during installations:
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1. Install `torch` and `triton`. Go to https://pytorch.org to install it. For example `pip install torch torchvision torchaudio triton`
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2. Confirm if CUDA is installated correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.
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3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. 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.
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4. 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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## 📜 [Documentation](https://docs.unsloth.ai)
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## 📜 Documentation
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- Go to our official [Documentation](https://docs.unsloth.ai) for saving to GGUF, checkpointing, evaluation and more!
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- We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!
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- We're in 🤗Hugging Face's official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
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@ -439,8 +429,8 @@ 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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- [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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- [atgctg](https://github.com/atgctg) for syntax highlighting
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