See all our notebooks for: Kaggle, GRPO, TTS, embedding & Vision
查看我们所有的 Notebook，用于：Kaggle, GRPO, TTS (Text-to-Speech), embedding (嵌入) & Vision（视觉）
See all our models and all our notebooks
查看我们所有的模型和我们所有的 Notebook
See detailed documentation for Unsloth here
在这里查看 Unsloth 的详细文档

pip install unsloth

Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. Guide + notebooks
现在支持 Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B。指南 + Notebook
Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
使用减少 35% 的 VRAM，以 12 倍的速度训练 MoE (Mixture of Experts) LLM (Large Language Models) - DeepSeek、GLM、Qwen 和 gpt-oss。博客
Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
Embedding (嵌入) 模型：Unsloth 现在支持约 1.8-3.3 倍速的 embedding (嵌入) 微调。博客 • Notebook
New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
通过我们新的批处理算法，实现了比所有其他设置长 7 倍的上下文 RL (Reinforcement Learning)。博客
New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
新的 RoPE (Rotary Positional Embedding) & MLP (Multilayer Perceptron) Triton Kernels (内核) & Padding Free + Packing (填充免费 + 打包): 3 倍速的训练和 30% 的 VRAM 减少。博客
500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
500K 上下文：现在可以在 80GB GPU 上训练具有 >500K 上下文的 20B 模型。博客
FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL
FP8 (8-bit Floating Point) & Vision (视觉) RL (强化学习): 现在可以在消费级 GPU 上进行 FP8 (8-bit Floating Point) 和 VLM (Vision Language Model) GRPO。FP8 博客 • Vision RL
Docker: Use Unsloth with no setup & environment issues with our new image. Guide • Docker image
Docker：使用我们的新镜像，无需设置和环境问题即可使用 Unsloth。指南 • Docker 镜像
gpt-oss by OpenAI: Read our RL blog, Flex Attention blog and Guide.
OpenAI 的 gpt-oss：阅读我们的 RL (强化学习) 博客、Flex Attention (弹性注意力) 博客和指南。

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, 4-bit, 16-bit and FP8 training
支持全微调 (full-finetuning)、预训练 (pretraining)、4-bit、16-bit 和 FP8 训练
Supports all models including TTS, multimodal, embedding and more! Any model that works in transformers, works in Unsloth.
支持所有模型，包括 TTS、多模态 (multimodal)、嵌入 (embedding) 等！任何在 transformers 中工作的模型都可以在 Unsloth 中工作。
The most efficient library for Reinforcement Learning (RL), using 80% less VRAM. Supports GRPO, GSPO, DrGRPO, DAPO etc.
最高效的强化学习 (Reinforcement Learning, RL) 库，使用少 80% 的 VRAM。支持 GRPO、GSPO、DrGRPO、DAPO 等。
0% loss in accuracy - no approximation methods - all exact.
0% 精度损失 - 没有近似方法 - 所有都精确。
Export and deploy your model to GGUF llama.cpp, vLLM, SGLang and Hugging Face.
将您的模型导出并部署到 GGUF llama.cpp、vLLM、SGLang 和 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)
支持 NVIDIA (2018 年以来)、AMD 和 Intel GPU。最低 CUDA Capability 7.0 (V100、T4、Titan V、RTX 20、30、40x、A100、H100、L40 等)
Works on Linux, WSL and Windows
适用于 Linux、WSL 和 Windows
All kernels written in OpenAI's Triton language. Manual backprop engine.
所有内核都用 OpenAI 的 Triton 语言编写。手动反向传播引擎 (Manual backprop engine)。
If you trained a model with 🦥Unsloth, you can use this cool sticker!  
如果你用 🦥Unsloth 训练了一个模型，你可以使用这个酷炫的贴纸！  

pip install unsloth

pip install --upgrade --force-reinstall --no-cache-dir unsloth unsloth_zoo

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

安装 Miniconda（或 Anaconda）：推荐使用 Miniconda。安装 Miniconda 或 Anaconda，然后打开 Anaconda PowerShell Prompt 继续。

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

创建 Conda 环境：为 Unsloth 创建并激活一个新的 Python 3.12 环境。 conda create --name unsloth_env python==3.12 -y conda activate unsloth_env

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.

检查您的 GPU 和 CUDA 版本：运行 nvidia-smi 以确认检测到您的 NVIDIA GPU，并记下输出中显示的 CUDA 版本。如果 nvidia-smi 不起作用，请重新安装最新的 NVIDIA 驱动程序。

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

安装 PyTorch：安装与您的 CUDA 版本匹配的 PyTorch 的 Windows pip 构建版本。使用 Install PyTorch 选择适合您系统的正确命令，然后验证 PyTorch 是否可以看到您的 GPU。 import torch print(torch.cuda.is_available()) A = torch.ones((10, 10), device="cuda") B = torch.ones((10, 10), device="cuda") A @ B

Install Unsloth: Only install Unsloth after PyTorch is working correctly.

pip install unsloth

安装 Unsloth：只有在 PyTorch 正常工作后才安装 Unsloth。 pip install unsloth

python -m venv unsloth
source unsloth/bin/activate
pip install unsloth

Install torch and triton. Go to https://pytorch.org to install it. For example pip install torch torchvision torchaudio triton
安装 torch 和 triton。访问 https://pytorch.org 安装它。例如 pip install torch torchvision torchaudio triton
Confirm if CUDA is installed correctly. Try nvcc. If that fails, you need to install cudatoolkit or CUDA drivers.
确认 CUDA 是否已正确安装。尝试 nvcc。如果失败，您需要安装 cudatoolkit 或 CUDA 驱动程序。
Install xformers manually via:
通过以下方式手动安装 xformers：

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`

For GRPO runs, you can try installing vllm and seeing if pip install vllm succeeds.
对于 GRPO 运行，您可以尝试安装 vllm 并查看 pip install vllm 是否成功。
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.
仔细检查您的 Python、CUDA、CUDNN、torch、triton 和 xformers 版本是否彼此兼容。PyTorch Compatibility Matrix 可能会很有用。
Finally, install bitsandbytes and check it with python -m bitsandbytes
最后，安装 bitsandbytes 并使用 python -m bitsandbytes 检查它

conda create --name unsloth_env python==3.12 -y
conda activate unsloth_env

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

pip install --upgrade pip
pip install "unsloth[cu121-torch240] @ git+https://github.com/unslothai/unsloth.git"

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

pip install --upgrade pip
pip install "unsloth[cu126-torch2100] @ git+https://github.com/unslothai/unsloth.git"

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"

wget -qO- https://raw.githubusercontent.com/unslothai/unsloth/main/unsloth/_auto_install.py | 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 run -d -e JUPYTER_PASSWORD="mypassword" \
  -p 8888:8888 -p 2222:22 \
  -v $(pwd)/work:/workspace/work \
  --gpus all \
  unsloth/unsloth

Go to our official Documentation for running models, saving to GGUF, checkpointing, evaluation and more!
访问我们的官方文档 (Documentation) 以运行模型、保存为 GGUF、检查点、评估等！
Read our Guides for: Fine-tuning, Reinforcement Learning, Text-to-Speech (TTS), Vision and any model.
阅读我们的指南，了解：微调、强化学习、文本到语音 (TTS) (Text-to-Speech (TTS))、视觉和任何模型。
We support Huggingface's transformers, TRL, Trainer, Seq2SeqTrainer and Pytorch code.
我们支持 Huggingface (Huggingface) 的 transformers (transformers)、TRL (TRL)、Trainer (Trainer)、Seq2SeqTrainer (Seq2SeqTrainer) 和 Pytorch (Pytorch) 代码。

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

gpt-oss GRPO notebook: Link
gpt-oss GRPO (GRPO) 笔记本：链接
FP8 Qwen3-8B GRPO notebook (L4): Link
FP8 (FP8) Qwen3-8B GRPO (GRPO) 笔记本 (L4)：链接
Qwen3-VL GSPO notebook: Link
Qwen3-VL GSPO (GSPO) 笔记本：链接
Advanced Qwen3 GRPO notebook: Link
高级 Qwen3 GRPO (GRPO) 笔记本：链接
ORPO notebook: Link
ORPO (ORPO) 笔记本：链接
DPO Zephyr notebook: Link
DPO (DPO) Zephyr 笔记本：链接
KTO notebook: Link
KTO (KTO) 笔记本：链接
SimPO notebook: Link
SimPO (SimPO) 笔记本：链接

For our most detailed benchmarks, read our Llama 3.3 Blog.
有关我们最详细的基准测试，请阅读我们的 Llama 3.3 博客 (Llama 3.3 Blog)。
Benchmarking of Unsloth was also conducted by 🤗Hugging Face.
🤗Hugging Face (🤗Hugging Face) 也对 Unsloth 进行了基准测试。