✨ Train for Free

Notebooks are beginner friendly. Read our guide. Add dataset, run, then export your trained model to GGUF, Ollama, vLLM 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

⚡ Quickstart

Linux or WSL

pip install unsloth

Windows

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

Docker

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

Blackwell & DGX Spark

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

🦥 Unsloth News

• DeepSeek-OCR: Fine-tune to improve DeepSeek-OCR’s 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
• Quantization-Aware Training: We collabed with Pytorch for QAT, recovering as much 70% accuracy. Read blog
• 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
• gpt-oss by OpenAI: For details on Unsloth Flex Attention, long-context training, bug fixes, Read our Guide. 20B works on a 14GB GPU and 120B on 65GB VRAM. gpt-oss uploads.
• Gemma 3n by Google: Read Blog. We uploaded GGUFs, 4-bit models.
• Text-to-Speech (TTS) is now supported, including sesame/csm-1b and STT openai/whisper-large-v3.
• Qwen3 is now supported. Qwen3-30B-A3B fits on 17.5GB VRAM.
• Introducing Dynamic 2.0 quants that set new benchmarks on 5-shot MMLU & Aider Polyglot.
• EVERYTHING is now supported - all models (TTS, BERT, Mamba), FFT, etc. MultiGPU coming soon. Enable FFT with full_finetuning = True, 8-bit with load_in_8bit = True.

🔗 Links and Resources

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

⭐ Key Features

• Supports full-finetuning, pretraining, 4b-bit, 16-bit and 8-bit 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!  

💾 Install Unsloth

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

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 for advanced pip install instructions.

Windows Installation

1. Install NVIDIA Video Driver: You should install the latest version of your GPUs driver. Download drivers here: NVIDIA GPU Drive.

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:

pip install unsloth

Notes

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,
    ...
  )

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

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

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 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. 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

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 and CUDA versions.

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.

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 2.5 and CUDA 12.4, use:

pip install --upgrade pip
pip install "unsloth[cu124-torch250] @ 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: 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"): 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'
else: raise RuntimeError(f"Torch = {v} too new!")
if v > V('2.6.9') and cuda not in ("11.8", "12.6", "12.8"): raise RuntimeError(f"CUDA = {cuda} not supported!")
x = x.format(cuda.replace(".", ""), "-ampere" if is_ampere else "")
print(f'pip install --upgrade pip && pip install "unsloth[{x}] @ git+https://github.com/unslothai/unsloth.git"')

Docker Installation

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!

📜 Documentation

• 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:

from unsloth import FastLanguageModel, FastModel
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 = 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 quantization
    load_in_16bit = False, # [NEW!] 16-bit LoRA
    full_finetuning = False, # Use for full fine-tuning.
    # 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://docs.unsloth.ai for advanced tips like
# (1) Saving to GGUF / merging to 16bit for vLLM
# (2) Continued training from a saved LoRA adapter
# (3) Adding an evaluation loop / OOMs
# (4) Customized chat templates

💡 Reinforcement Learning

RL including GRPO, GSPO, 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
• ORPO notebook: Link
• DPO Zephyr notebook: Link
• KTO notebook: Link
• SimPO notebook: Link

🥇 Performance Benchmarking

• 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):

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.

Citation

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}
}

Thank You to

• 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!