Notebooks are beginner friendly. Read our guide. Add dataset, run, then export your trained model to GGUF, llama.cpp, Ollama, vLLM, SGLang or Hugging Face.

• See all our notebooks for: Kaggle, GRPO, TTS & Vision
• See all our models and all our notebooks
• See detailed documentation for Unsloth here

pip install unsloth

For Windows, pip install unsloth works only if you have Pytorch installed. Read our Windows Guide.

Use our official Unsloth Docker imageunsloth/unsloth container. Read our Docker Guide.

For RTX 50x, B200, 6000 GPUs: pip install unsloth. Read our Blackwell Guide and DGX Spark Guide for more details.

• New RoPE & MLP Triton Kernels & Auto Packing: 3x faster training & 30% less VRAM. Blog
• Ministral 3 by Mistral: Run Ministral 3 or fine-tune with our vision or RL sodoku notebook. Guide • Notebooks
• 500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
• FP8 Reinforcement Learning: You can now do FP8 GRPO on consumer GPUs. Blog • Notebook
• DeepSeek-OCR: Fine-tune to improve language understanding by 89%. Guide • Notebook
• Docker: Use Unsloth with no setup & environment issues with our new image. Guide • Docker image
• gpt-oss RL: Introducing the fastest possible inference for gpt-oss RL! Read blog
• Vision RL: You can now train VLMs with GRPO or GSPO in Unsloth! Read guide
• gpt-oss by OpenAI: Read our Unsloth Flex Attention blog and gpt-oss Guide. 20B works on 14GB VRAM. 120B on 65GB.

Type	Links
r/unsloth Reddit	Join Reddit community
📚 Documentation & Wiki	Read Our Docs
Twitter (aka X)	Follow us on X
💾 Installation	Pip & Docker Install
🔮 Our Models	Unsloth Catalog
✍️ Blog	Read our Blogs

• Supports full-finetuning, pretraining, 4b-bit, 16-bit and FP8 training
• Supports all models including TTS, multimodal, BERT and more! Any model that works in transformers, works in Unsloth.
• The most efficient library for Reinforcement Learning (RL), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc.
• 0% loss in accuracy - no approximation methods - all exact.
• Supports NVIDIA (since 2018), AMD and 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
• All kernels written in OpenAI's Triton language. Manual backprop engine.
• If you trained a model with 🦥Unsloth, you can use this cool sticker!

You can also see our docs for more detailed installation and updating instructions here.

Unsloth supports Python 3.13 or lower.

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 for advanced pip install instructions.

1. Install NVIDIA Video Driver: You should install the latest driver for your GPU. Download drivers here: NVIDIA GPU Driver.

2. Install Visual Studio C++: 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. For detailed instructions with options, see here.

3. Install CUDA Toolkit: Follow the instructions to install CUDA Toolkit.

4. Install PyTorch: You will need the correct version of PyTorch that is compatible with your CUDA drivers, so make sure to select them carefully. Install PyTorch.

5. Install Unsloth:

pipinstallunsloth

To run Unsloth directly on Windows:

• Install Triton from this Windows fork and follow the instructions here (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)
• In the SFTConfig, set dataset_num_proc=1 to avoid a crashing issue:

SFTConfig( dataset_num_proc=1, ... )

For advanced installation instructions or if you see weird errors during installations:

First try using an isolated environment via then pip install unsloth

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:

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` 

5. For GRPO runs, you can try installing vllm and seeing if pip install vllm succeeds.
6. Double check that your versions of Python, CUDA, CUDNN, torch, triton, and xformers are compatible with one another. The PyTorch Compatibility Matrix may be useful.
7. Finally, install bitsandbytes and check it with python -m bitsandbytes

⚠️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.

conda create --name unsloth_env \ python=3.11 \ pytorch-cuda=12.1 \ pytorch cudatoolkit xformers -c pytorch -c nvidia -c xformers \ -y conda activate unsloth_env pip install unsloth

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

⚠️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 and CUDA versions.

For other torch versions, we support torch211, torch212, torch220, torch230, torch240, torch250, torch260, torch270, torch280, torch290 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.

For example, if you have torch 2.4 and CUDA 12.1, use:

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:

pip install --upgrade pip pip install "unsloth[cu130-torch290] @ git+https://github.com/unslothai/unsloth.git"

And other examples:

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:

wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | python -

Or, run the below manually in a Python REPL:

try: importtorchexcept: raiseImportError('Install torch via `pip install torch`') frompackaging.versionimportVersionasVimportrev=V(re.match(r"[0-9\.]{3,}", torch.__version__).group(0)) cuda=str(torch.version.cuda) is_ampere=torch.cuda.get_device_capability()[0] >=8USE_ABI=torch._C._GLIBCXX_USE_CXX11_ABIifcudanotin ("11.8", "12.1", "12.4", "12.6", "12.8", "13.0"): raiseRuntimeError(f"CUDA = {cuda} not supported!") ifv<=V('2.1.0'): raiseRuntimeError(f"Torch = {v} too old!") elifv<=V('2.1.1'): x='cu{}{}-torch211'elifv<=V('2.1.2'): x='cu{}{}-torch212'elifv<V('2.3.0'): x='cu{}{}-torch220'elifv<V('2.4.0'): x='cu{}{}-torch230'elifv<V('2.5.0'): x='cu{}{}-torch240'elifv<V('2.5.1'): x='cu{}{}-torch250'elifv<=V('2.5.1'): x='cu{}{}-torch251'elifv<V('2.7.0'): x='cu{}{}-torch260'elifv<V('2.7.9'): x='cu{}{}-torch270'elifv<V('2.8.0'): x='cu{}{}-torch271'elifv<V('2.8.9'): x='cu{}{}-torch280'elifv<V('2.9.1'): x='cu{}{}-torch290'elifv<V('2.9.2'): x='cu{}{}-torch291'else: raiseRuntimeError(f"Torch = {v} too new!") ifv>V('2.6.9') andcudanotin ("11.8", "12.6", "12.8", "13.0"): raiseRuntimeError(f"CUDA = {cuda} not supported!") x=x.format(cuda.replace(".", ""), "-ampere"ifFalseelse"") # is_ampere is broken due to flash-attnprint(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')

You can use our pre-built Docker container with all dependencies to use Unsloth instantly with no setup required. Read our guide.

This container requires installing NVIDIA's Container Toolkit.

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!

• Go to our official Documentation for running models, saving to GGUF, checkpointing, evaluation and more!
• Read our Guides for: Fine-tuning, Reinforcement Learning, Text-to-Speech (TTS), Vision and any model.
• We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code.

Unsloth example code to fine-tune gpt-oss-20b:

fromunslothimportFastLanguageModel, FastModelimporttorchfromtrlimportSFTTrainer, SFTConfigfromdatasetsimportload_datasetmax_seq_length=2048# Supports RoPE Scaling internally, so choose any!# Get LAION dataseturl="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/unslothmodel, tokenizer=FastModel.from_pretrained( model_name="unsloth/gpt-oss-20b", max_seq_length=2048, # Choose any for long context!load_in_4bit=True, # 4-bit quantization. False = 16-bit LoRA.load_in_8bit=False, # 8-bit quantizationload_in_16bit=False, # [NEW!] 16-bit LoRAfull_finetuning=False, # Use for full fine-tuning.# token = "hf_...", # use one if using gated models ) # Do model patching and add fast LoRA weightsmodel=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 optimizedbias="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 contextrandom_state=3407, max_seq_length=max_seq_length, use_rslora=False, # We support rank stabilized LoRAloftq_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://docs.unsloth.ai 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

RL including GRPO, GSPO, FP8 traning, DrGRPO, DAPO, PPO, Reward Modelling, Online DPO all work with Unsloth. Read our Reinforcement Learning Guide or our advanced RL docs for batching, generation & training parameters.

List of RL notebooks:

• gpt-oss GSPO notebook: Link
• Qwen2.5-VL GSPO notebook: Link
• Advanced Qwen3 GRPO notebook: Link
• FP8 Qwen3-8B GRPO notebook (L4): Link
• ORPO notebook: Link
• DPO Zephyr notebook: Link
• KTO notebook: Link
• SimPO notebook: Link

• For our most detailed benchmarks, read our Llama 3.3 Blog.
• Benchmarking of Unsloth was also conducted by 🤗Hugging Face.

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):

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.

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.

You can cite the Unsloth repo as follows:

@software{unsloth, author = {Daniel Han, Michael Han and Unsloth team}, title = {Unsloth}, url = {http://github.com/unslothai/unsloth}, year = {2023} }

• The llama.cpp library that lets users save models with Unsloth
• The Hugging Face team and their libraries: transformers and TRL
• The Pytorch and Torch AO team for their contributions
• Erik for his help adding Apple's ML Cross Entropy in Unsloth
• Etherl for adding support for TTS, diffusion and BERT models
• And of course for every single person who has contributed or has used Unsloth!