### Train gpt-oss, DeepSeek, Gemma, Qwen & Llama 2x faster with 70% less VRAM! ![](https://i.ibb.co/sJ7RhGG/image-41.png)

## ✨ Train for Free Notebooks are beginner friendly. Read our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model. | Model | Free Notebooks | Performance | Memory use | |-----------|---------|--------|----------| | **Qwen3.5 (4B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb) | 1.5x faster | 60% less | | **gpt-oss (20B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-Fine-tuning.ipynb) | 2x faster | 70% less | | **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 | | **Qwen3: Advanced GRPO** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) | 2x faster | 50% less | | **Gemma 3 (4B) Vision** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B)-Vision.ipynb) | 1.7x faster | 60% less | | **embeddinggemma (300M)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/EmbeddingGemma_(300M).ipynb) | 2x faster | 20% less | | **Mistral Ministral 3 (3B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Ministral_3_VL_(3B)_Vision.ipynb) | 1.5x faster | 60% less | | **Llama 3.1 (8B) Alpaca** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb) | 2x faster | 70% less | | **Llama 3.2 Conversational** | [▶️ 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 | | **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 | - See all our notebooks for: [Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks), [GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks), [TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks), [embedding](https://unsloth.ai/docs/new/embedding-finetuning) & [Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks) - See [all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks) - See detailed documentation for Unsloth [here](https://unsloth.ai/docs) ## ⚡ Quickstart ### Linux or WSL ```bash pip install unsloth ``` ### Windows 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). ### Docker 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). ### AMD, Intel, Blackwell & DGX Spark For RTX 50x, B200, 6000 GPUs: `pip install unsloth`. Read our guides for: [Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth).
To install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/get-started/install/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel). ## 🦥 Unsloth News - **Qwen3.5** - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. [Guide + notebooks](https://unsloth.ai/docs/models/qwen3.5/fine-tune) - Train **MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. [Blog](https://unsloth.ai/docs/new/faster-moe) - **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) - New **7x longer context RL** vs. all other setups, via our new batching algorithms. [Blog](https://unsloth.ai/docs/new/grpo-long-context) - New RoPE & MLP **Triton Kernels** & **Padding Free + Packing**: 3x faster training & 30% less VRAM. [Blog](https://unsloth.ai/docs/new/3x-faster-training-packing) - **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) - **FP8 & Vision RL**: You can now do FP8 & VLM GRPO on consumer GPUs. [FP8 Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl) - **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) - **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 [Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune).

Click for more news

- **Quantization-Aware Training**: We collabed with Pytorch, recovering ~70% accuracy. [Read blog](https://unsloth.ai/docs/blog/quantization-aware-training-qat) - **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) - **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) - **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). - **[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`. - **[Qwen3](https://unsloth.ai/docs/models/qwen3-how-to-run-and-fine-tune)** is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM. - 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. - [**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`. - 📣 [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). - 📣 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! - 📣 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) - 📣 **[Llama 4](https://unsloth.ai/blog/llama4)** by Meta, including Scout & Maverick are now supported. - 📣 [Phi-4](https://unsloth.ai/blog/phi4) by Microsoft: We also [fixed bugs](https://unsloth.ai/blog/phi4) in Phi-4 and [uploaded GGUFs, 4-bit](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa). - 📣 [Vision models](https://unsloth.ai/blog/vision) now supported! [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb), [Qwen 2.5 VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) and [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_(12B)-Vision.ipynb) - 📣 [Llama 3.3 (70B)](https://huggingface.co/collections/unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f), Meta's latest model is supported. - 📣 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. - 📣 We found and helped fix a [gradient accumulation bug](https://unsloth.ai/blog/gradient)! Please update Unsloth and transformers. - 📣 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)!

## 🔗 Links and Resources | Type | Links | | ----------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------ | |

**Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai) | | 💾 **Installation** | [Pip & Docker Install](https://unsloth.ai/docs/get-started/install) | | 🔮 **Our Models** | [Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog) | | ✍️ **Blog** | [Read our Blogs](https://unsloth.ai/blog) | ## ⭐ Key Features * Supports **full-finetuning**, pretraining, 4-bit, 16-bit and **FP8** training * 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. * 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. * **0% loss in accuracy** - no approximation methods - all exact. * Export and [deploy your model](https://unsloth.ai/docs/basics/inference-and-deployment) to [GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf) llama.cpp, [vLLM](https://unsloth.ai/docs/basics/inference-and-deployment/vllm-guide), [SGLang](https://unsloth.ai/docs/basics/inference-and-deployment/sglang-guide) and Hugging Face. * Supports NVIDIA (since 2018), [AMD](https://unsloth.ai/docs/get-started/install/amd) and [Intel](https://unsloth.ai/docs/get-started/install/intel) GPUs. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) * Works on **Linux**, WSL and **[Windows](https://unsloth.ai/docs/get-started/install/windows-installation)** * All kernels written in OpenAI's Triton language. Manual backprop engine. * If you trained a model with 🦥Unsloth, you can use this cool sticker!

## 💾 Install Unsloth You can also see our docs for more detailed installation and updating instructions [here](https://unsloth.ai/docs/get-started/install). Unsloth supports Python 3.13 or lower. ### Pip Installation **Install with pip (recommended) for Linux devices:** ``` pip install unsloth ``` **To update Unsloth:** ``` pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo ``` See [here](#advanced-pip-installation) for advanced pip install instructions. ### Windows Installation For this method, we will be utilizing Anaconda. You can view the [full guide with screenshots here](https://unsloth.ai/docs/get-started/install/windows-installation). 1. **Install Miniconda (or Anaconda):** Miniconda is recommended. Install [Miniconda](https://www.anaconda.com/docs/getting-started/miniconda/install) or [Anaconda](https://www.anaconda.com/download), then open Anaconda PowerShell Prompt to continue. 2. **Create a Conda Environment:** Create and activate a fresh Python 3.12 environment for Unsloth. ```bash conda create --name unsloth_env python==3.12 -y conda activate unsloth_env ``` 3. **Check Your GPU and CUDA Version:** Run `nvidia-smi` to confirm that your NVIDIA GPU is detected and note the CUDA version shown in the output. If `nvidia-smi` does not work, reinstall the latest [NVIDIA drivers](https://www.nvidia.com/en-us/drivers/). 4. **Install PyTorch:** Install the Windows pip build of PyTorch that matches your CUDA version. Use [Install PyTorch](https://pytorch.org/get-started/locally/) to select the correct command for your system, then verify that PyTorch can see your GPU. ```python import torch print(torch.cuda.is_available()) A = torch.ones((10, 10), device="cuda") B = torch.ones((10, 10), device="cuda") A @ B ``` 5. **Install Unsloth:** Only install Unsloth after PyTorch is working correctly. ```bash pip install unsloth ``` #### Advanced/Troubleshooting For **advanced installation instructions** or if you see weird errors during installations: First try using an isolated environment via then `pip install unsloth` ```bash python -m venv unsloth source unsloth/bin/activate pip install unsloth ``` 1. Install `torch` and `triton`. Go to https://pytorch.org to install it. For example `pip install torch torchvision torchaudio triton` 2. Confirm if CUDA is installed correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers. 3. Install `xformers` manually via: ```bash pip install ninja pip install -v --no-build-isolation -U git+https://github.com/facebookresearch/xformers.git@main#egg=xformers ``` 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` 4. For GRPO runs, you can try installing `vllm` and seeing if `pip install vllm` succeeds. 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. 6. Finally, install `bitsandbytes` and check it with `python -m bitsandbytes` ### Conda Installation (Optional) `⚠️Only use Conda if you have it. If not, use Pip`. We support `python=3.10,3.11,3.12,3.13`. ```bash conda create --name unsloth_env python==3.12 -y conda activate unsloth_env ``` Use `nvidia-smi` to get the correct CUDA version like 13.0 which becomes `cu130` ```bash pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130 pip3 install unsloth ```

If you're looking to install Conda in a Linux environment, read here, or run the below 🔽

```bash mkdir -p ~/miniconda3 wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3 rm -rf ~/miniconda3/miniconda.sh ~/miniconda3/bin/conda init bash ~/miniconda3/bin/conda init zsh ```

### Advanced Pip Installation `⚠️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 2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,2.10` and CUDA versions. For other torch versions, we support `torch211`, `torch212`, `torch220`, `torch230`, `torch240`, `torch250`, `torch260`, `torch270`, `torch280`, `torch290`, `torch2100` 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`. Note: torch 2.10 only supports CUDA 12.6, 12.8, and 13.0. For example, if you have `torch 2.4` and `CUDA 12.1`, use: ```bash pip install --upgrade pip pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git" ``` Another example, if you have `torch 2.9` and `CUDA 13.0`, use: ```bash pip install --upgrade pip pip install "unsloth[cu130-torch290] @ git+https://github.com/unslothai/unsloth.git" ``` Another example, if you have `torch 2.10` and `CUDA 12.6`, use: ```bash pip install --upgrade pip pip install "unsloth[cu126-torch2100] @ git+https://github.com/unslothai/unsloth.git" ``` And other examples: ```bash pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu118-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu118-torch240] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu121-torch250] @ git+https://github.com/unslothai/unsloth.git" pip install "unsloth[cu124-ampere-torch250] @ git+https://github.com/unslothai/unsloth.git" ``` Or, run the below in a terminal to get the **optimal** pip installation command: ```bash wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python - ``` Or, run the below manually in a Python REPL: ```python try: import torch except: raise ImportError('Install torch via `pip install torch`') from packaging.version import Version as V import re v = V(re.match(r"[0-9\.]{3,}", torch.__version__).group(0)) cuda = str(torch.version.cuda) is_ampere = torch.cuda.get_device_capability()[0] >= 8 USE_ABI = torch._C._GLIBCXX_USE_CXX11_ABI if cuda not in ("11.8", "12.1", "12.4", "12.6", "12.8", "13.0"): raise RuntimeError(f"CUDA = {cuda} not supported!") if v <= V('2.1.0'): raise RuntimeError(f"Torch = {v} too old!") elif v <= V('2.1.1'): x = 'cu{}{}-torch211' elif v <= V('2.1.2'): x = 'cu{}{}-torch212' elif v < V('2.3.0'): x = 'cu{}{}-torch220' elif v < V('2.4.0'): x = 'cu{}{}-torch230' elif v < V('2.5.0'): x = 'cu{}{}-torch240' elif v < V('2.5.1'): x = 'cu{}{}-torch250' elif v <= V('2.5.1'): x = 'cu{}{}-torch251' elif v < V('2.7.0'): x = 'cu{}{}-torch260' elif v < V('2.7.9'): x = 'cu{}{}-torch270' elif v < V('2.8.0'): x = 'cu{}{}-torch271' elif v < V('2.8.9'): x = 'cu{}{}-torch280' elif v < V('2.9.1'): x = 'cu{}{}-torch290' elif v < V('2.9.2'): x = 'cu{}{}-torch291' elif v < V('2.10.1'): x = 'cu{}{}-torch2100' else: raise RuntimeError(f"Torch = {v} too new!") if v > V('2.6.9') and cuda not in ("11.8", "12.6", "12.8", "13.0"): raise RuntimeError(f"CUDA = {cuda} not supported!") if v >= V('2.10.0') and cuda not in ("12.6", "12.8", "13.0"): raise RuntimeError(f"Torch 2.10 requires CUDA 12.6, 12.8, or 13.0! Got CUDA = {cuda}") x = x.format(cuda.replace(".", ""), "-ampere" if False else "") # is_ampere is broken due to flash-attn print(f'pip install --upgrade pip && pip install --no-deps git+https://github.com/unslothai/unsloth-zoo.git && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git" --no-build-isolation') ``` ### Docker Installation You can use our pre-built Docker container with all dependencies to use Unsloth instantly with no setup required. [Read our guide](https://unsloth.ai/docs/get-started/install/docker). This container requires installing [NVIDIA's Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). ```bash docker run -d -e JUPYTER_PASSWORD="mypassword" \ -p 8888:8888 -p 2222:22 \ -v $(pwd)/work:/workspace/work \ --gpus all \ unsloth/unsloth ``` Access Jupyter Lab at `http://localhost:8888` and start fine-tuning! ## 📜 Documentation * 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! * 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). * We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code. Unsloth example code to fine-tune gpt-oss-20b: ```python from unsloth import FastLanguageModel, FastModel, FastVisionModel import torch from trl import SFTTrainer, SFTConfig from datasets import load_dataset max_seq_length = 2048 # Supports RoPE Scaling internally, so choose any! # Get LAION dataset url = "https://huggingface.co/datasets/laion/OIG/resolve/main/unified_chip2.jsonl" dataset = load_dataset("json", data_files = {"train" : url}, split = "train") # 4bit pre quantized models we support for 4x faster downloading + no OOMs. fourbit_models = [ "unsloth/gpt-oss-20b-unsloth-bnb-4bit", #or choose any model ] # More models at https://huggingface.co/unsloth model, tokenizer = FastLanguageModel.from_pretrained( model_name = "unsloth/gpt-oss-20b", max_seq_length = max_seq_length, # Choose any for long context! load_in_4bit = True, # 4-bit quantization. False = 16-bit LoRA. load_in_8bit = False, # 8-bit quantization load_in_16bit = False, # 16-bit LoRA full_finetuning = False, # Use for full fine-tuning. trust_remote_code = False, # Enable to support new models # token = "hf_...", # use one if using gated models ) # Do model patching and add fast LoRA weights model = FastLanguageModel.get_peft_model( model, r = 16, target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj",], lora_alpha = 16, lora_dropout = 0, # Supports any, but = 0 is optimized bias = "none", # Supports any, but = "none" is optimized # [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes! use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context random_state = 3407, max_seq_length = max_seq_length, use_rslora = False, # We support rank stabilized LoRA loftq_config = None, # And LoftQ ) trainer = SFTTrainer( model = model, train_dataset = dataset, tokenizer = tokenizer, args = SFTConfig( max_seq_length = max_seq_length, per_device_train_batch_size = 2, gradient_accumulation_steps = 4, warmup_steps = 10, max_steps = 60, logging_steps = 1, output_dir = "outputs", optim = "adamw_8bit", seed = 3407, ), ) trainer.train() # Go to https://unsloth.ai/docs for advanced tips like # (1) Saving to GGUF / merging to 16bit for vLLM or SGLang # (2) Continued training from a saved LoRA adapter # (3) Adding an evaluation loop / OOMs # (4) Customized chat templates ``` ## 💡 Reinforcement Learning [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. 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. List of RL notebooks: - gpt-oss GRPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/gpt-oss-(20B)-GRPO.ipynb) - ***FP8*** Qwen3-8B GRPO notebook (L4): [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_8B_FP8_GRPO.ipynb) - Qwen3-VL GSPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_VL_(8B)-Vision-GRPO.ipynb) - Advanced Qwen3 GRPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_(4B)-GRPO.ipynb) - ORPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-ORPO.ipynb) - DPO Zephyr notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb) - KTO notebook: [Link](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing) - SimPO notebook: [Link](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing) ## 🥇 Performance Benchmarking - For our most detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3). - Benchmarking of Unsloth was also conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl). We tested using the Alpaca Dataset, a batch size of 2, gradient accumulation steps of 4, rank = 32, and applied QLoRA on all linear layers (q, k, v, o, gate, up, down): | Model | VRAM | 🦥 Unsloth speed | 🦥 VRAM reduction | 🦥 Longer context | 😊 Hugging Face + FA2 | |----------------|-------|-----------------|----------------|----------------|--------------------| | Llama 3.3 (70B)| 80GB | 2x | >75% | 13x longer | 1x | | Llama 3.1 (8B) | 80GB | 2x | >70% | 12x longer | 1x | ### Context length benchmarks #### Llama 3.1 (8B) max. context length We tested Llama 3.1 (8B) Instruct and did 4bit QLoRA on all linear layers (Q, K, V, O, gate, up and down) with rank = 32 with a batch size of 1. We padded all sequences to a certain maximum sequence length to mimic long context finetuning workloads. | GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 | |----------|-----------------------|-----------------| | 8 GB | 2,972 | OOM | | 12 GB | 21,848 | 932 | | 16 GB | 40,724 | 2,551 | | 24 GB | 78,475 | 5,789 | | 40 GB | 153,977 | 12,264 | | 48 GB | 191,728 | 15,502 | | 80 GB | 342,733 | 28,454 | #### Llama 3.3 (70B) max. context length We tested Llama 3.3 (70B) Instruct on a 80GB A100 and did 4bit QLoRA on all linear layers (Q, K, V, O, gate, up and down) with rank = 32 with a batch size of 1. We padded all sequences to a certain maximum sequence length to mimic long context finetuning workloads. | GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 | |----------|------------------------|------------------| | 48 GB | 12,106 | OOM | | 80 GB | 89,389 | 6,916 |
![](https://i.ibb.co/sJ7RhGG/image-41.png)
### Citation You can cite the Unsloth repo as follows: ```bibtex @software{unsloth, author = {Daniel Han, Michael Han and Unsloth team}, title = {Unsloth}, url = {https://github.com/unslothai/unsloth}, year = {2023} } ``` ### Thank You to - The [llama.cpp library](https://github.com/ggml-org/llama.cpp) that lets users save models with Unsloth - The Hugging Face team and their libraries: [transformers](https://github.com/huggingface/transformers) and [TRL](https://github.com/huggingface/trl) - The Pytorch and [Torch AO](https://github.com/unslothai/unsloth/pull/3391) team for their contributions - And of course for every single person who has contributed or has used Unsloth!