unsloth-zoo
Advanced tools
Notebooks are beginner friendly. Read our guide. Add your dataset, click "Run All", and export your finetuned model to GGUF, Ollama, vLLM or Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|---|---|---|---|
| gpt-oss (20B) | ▶️ Start for free | 1.5x faster | 70% less |
| Gemma 3n (4B) | ▶️ Start for free | 1.5x faster | 50% less |
| Qwen3 (14B) | ▶️ Start for free | 2x faster | 70% less |
| Qwen3 (4B): GRPO | ▶️ Start for free | 2x faster | 80% less |
| Gemma 3 (4B) | ▶️ Start for free | 1.6x faster | 60% less |
| Phi-4 (14B) | ▶️ Start for free | 2x faster | 70% less |
| Llama 3.2 Vision (11B) | ▶️ Start for free | 2x faster | 50% less |
| Llama 3.1 (8B) | ▶️ Start for free | 2x faster | 70% less |
| Mistral v0.3 (7B) | ▶️ Start for free | 2.2x faster | 75% less |
| Orpheus-TTS (3B) | ▶️ Start for free | 1.5x faster | 50% less |
pip install unsloth
For Windows, pip install unsloth works only if you have Pytorch installed. For more info, read our Windows Guide.
Use our official Unsloth Docker image unsloth/unsloth container. Read our Docker Guide.
For RTX 50x, B200, 6000 GPUs, simply do pip install unsloth. Read our Blackwell Guide for more details.
sesame/csm-1b and STT openai/whisper-large-v3.full_finetuning = True, 8-bit with load_in_8bit = True.📣 DeepSeek-R1 - run or fine-tune them with our guide. All model uploads: here.
📣 Introducing Long-context Reasoning (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! 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.
📣 Llama 4 by Meta, including Scout & Maverick are now supported.
📣 Phi-4 by Microsoft: We also fixed bugs in Phi-4 and uploaded GGUFs, 4-bit.
📣 Vision models now supported! Llama 3.2 Vision (11B), Qwen 2.5 VL (7B) and Pixtral (12B) 2409
📣 Llama 3.3 (70B), Meta's latest model is supported.
📣 We worked with Apple to add Cut Cross Entropy. 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! Please update Unsloth and transformers.
📣 We cut memory usage by a further 30% and now support 4x longer context windows!
| Type | Links |
|---|---|
| 📚 Documentation & Wiki | Read Our Docs |
 Twitter (aka X) | Follow us on X |
| 💾 Installation | Pip install |
| 🔮 Our Models | Unsloth Releases |
| ✍️ Blog | Read our Blogs |
 Reddit | Join our Reddit |
[!warning] Python 3.14 does not support Unsloth. Use 3.13 or lower.
You can also see our documentation for more detailed installation and updating instructions here.
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.
Install NVIDIA Video Driver: You should install the latest version of your GPUs driver. Download drivers here: NVIDIA GPU Drive.
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.
Install CUDA Toolkit: Follow the instructions to install CUDA Toolkit.
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.
Install Unsloth:
pip install unsloth
To run Unsloth directly on Windows:
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
torch and triton. Go to https://pytorch.org to install it. For example pip install torch torchvision torchaudio tritonnvcc. If that fails, you need to install cudatoolkit or CUDA drivers.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.
5. 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 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"')
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!
UNSLOTH_USE_MODELSCOPE=1, and install the modelscope library by: pip install modelscope -U.unsloth_cli.py also supports
UNSLOTH_USE_MODELSCOPE=1to download models and datasets. please remember to use the model and dataset id in the ModelScope community.
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/Meta-Llama-3.1-8B-bnb-4bit", # Llama-3.1 2x faster
"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"unsloth/Meta-Llama-3.1-70B-bnb-4bit",
"unsloth/Meta-Llama-3.1-405B-bnb-4bit", # 4bit for 405b!
"unsloth/Mistral-Small-Instruct-2409", # Mistral 22b 2x faster!
"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"unsloth/Phi-3.5-mini-instruct", # Phi-3.5 2x faster!
"unsloth/Phi-3-medium-4k-instruct",
"unsloth/gemma-2-9b-bnb-4bit",
"unsloth/gemma-2-27b-bnb-4bit", # Gemma 2x faster!
"unsloth/Llama-3.2-1B-bnb-4bit", # NEW! Llama 3.2 models
"unsloth/Llama-3.2-1B-Instruct-bnb-4bit",
"unsloth/Llama-3.2-3B-bnb-4bit",
"unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"unsloth/Llama-3.3-70B-Instruct-bnb-4bit" # NEW! Llama 3.3 70B!
] # More models at https://huggingface.co/unsloth
model, tokenizer = FastModel.from_pretrained(
model_name = "unsloth/gemma-3-4B-it",
max_seq_length = 2048, # Choose any for long context!
load_in_4bit = True, # 4 bit quantization to reduce memory
load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory
full_finetuning = False, # [NEW!] We have full finetuning now!
# 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://github.com/unslothai/unsloth/wiki 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
RL including DPO, GRPO, PPO, Reward Modelling, Online DPO all work with Unsloth. We're in 🤗Hugging Face's official docs! We're on the GRPO docs and the DPO docs! List of RL notebooks:
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # Optional set GPU device ID
from unsloth import FastLanguageModel
import torch
from trl import DPOTrainer, DPOConfig
max_seq_length = 2048
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/zephyr-sft-bnb-4bit",
max_seq_length = max_seq_length,
load_in_4bit = True,
)
# Do model patching and add fast LoRA weights
model = FastLanguageModel.get_peft_model(
model,
r = 64,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 64,
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,
)
dpo_trainer = DPOTrainer(
model = model,
ref_model = None,
train_dataset = YOUR_DATASET_HERE,
# eval_dataset = YOUR_DATASET_HERE,
tokenizer = tokenizer,
args = DPOConfig(
per_device_train_batch_size = 4,
gradient_accumulation_steps = 8,
warmup_ratio = 0.1,
num_train_epochs = 3,
logging_steps = 1,
optim = "adamw_8bit",
seed = 42,
output_dir = "outputs",
max_length = 1024,
max_prompt_length = 512,
beta = 0.1,
),
)
dpo_trainer.train()
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 |
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 |
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 |
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}
}
FAQs
Utils for Unsloth
We found that unsloth-zoo demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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