✨ Train for Free

Notebooks are beginner friendly. Read our guide. Add dataset, run, then deploy your trained model.

• See all our notebooks for: Kaggle, GRPO, TTS, embedding & 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.

AMD, Intel, Blackwell & DGX Spark

For RTX 50x, B200, 6000 GPUs: pip install unsloth. Read our guides for: Blackwell and DGX Spark.
To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.

🦥 Unsloth News

• Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. Guide + notebooks
• Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
• Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
• New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
• New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
• 500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
• FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL
• Docker: Use Unsloth with no setup & environment issues with our new image. Guide • Docker image
• gpt-oss by OpenAI: Read our RL blog, Flex Attention blog and Guide.

🔗 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, 4-bit, 16-bit and FP8 training
• Supports all models including TTS, multimodal, embedding 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.
• Export and deploy your model to GGUF llama.cpp, vLLM, SGLang and Hugging Face.
• 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

For this method, we will be utilizing Anaconda. You can view the full guide with screenshots here.

1. Install Miniconda (or Anaconda): Miniconda is recommended. Install Miniconda or Anaconda, then open Anaconda PowerShell Prompt to continue.

2. Create a Conda Environment: Create and activate a fresh Python 3.12 environment for Unsloth.

   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.

4. Install PyTorch: Install the Windows pip build of PyTorch that matches your CUDA version. Use Install PyTorch to select the correct command for your system, then verify that PyTorch can see your GPU.

   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.

   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

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. We support python=3.10,3.11,3.12,3.13.

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

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu130
pip3 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 2.2,2.3,2.4,2.5,2.6,2.7,2.8,2.9,2.10 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:

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"

Another example, if you have torch 2.10 and CUDA 12.6, use:

pip install --upgrade pip
pip install "unsloth[cu126-torch2100] @ 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", "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.

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, 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 including GRPO, GSPO, FP8 training, 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 GRPO notebook: Link
• FP8 Qwen3-8B GRPO notebook (L4): Link
• Qwen3-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 = {https://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
• And of course for every single person who has contributed or has used Unsloth!