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https://github.com/unslothai/unsloth

Finetune Llama 4, DeepSeek-R1, Gemma 3 & Reasoning LLMs 2x faster with 70% less memory! 🦥
https://github.com/unslothai/unsloth

deepseek deepseek-r1 fine-tuning finetuning gemma gemma2 gemma3 llama llama-4 llama3 llama4 llm llms lora mistral phi3 qlora unsloth

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Finetune Llama 4, DeepSeek-R1, Gemma 3 & Reasoning LLMs 2x faster with 70% less memory! 🦥

Awesome Lists containing this project

README 

        



  

    

    

    unsloth logo

  

  








### Finetune Llama 4, Gemma 3, Phi-4, Qwen 2.5 & Mistral 2x faster with 80% less VRAM!



![](https://i.ibb.co/sJ7RhGG/image-41.png)






## ✨ Finetune for Free



Notebooks are beginner friendly. Read our [guide](https://docs.unsloth.ai/get-started/fine-tuning-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 |

|-----------|---------|--------|----------|

| **GRPO (R1 reasoning)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)               | 2x faster | 80% less |

| **Gemma 3 (4B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Gemma3_(4B).ipynb)               | 1.6x faster | 60% less |

| **Llama 3.2 (3B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(1B_and_3B)-Conversational.ipynb)               | 2x faster | 70% less |

| **Phi-4 (14B)** | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4-Conversational.ipynb)               | 2x faster | 70% less |

| **Llama 3.2 Vision (11B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)               | 2x faster | 50% less |

| **Llama 3.1 (8B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb)               | 2x faster | 70% less |

| **Qwen 2.5 (7B)**      | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb)               | 2x faster | 70% less |

| **Mistral v0.3 (7B)**    | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb)               | 2.2x faster | 75% less |

| **Ollama**     | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)               | 1.9x faster | 60% less |

| **DPO Zephyr**     | [▶️ Start for free](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb)               | 1.9x faster | 50% less |



- See [all our notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks) and [all our models](https://docs.unsloth.ai/get-started/all-our-models)

- **Kaggle Notebooks** for [Llama 3.2 Kaggle notebook](https://www.kaggle.com/danielhanchen/kaggle-llama-3-2-1b-3b-unsloth-notebook), [Llama 3.1 (8B)](https://www.kaggle.com/danielhanchen/kaggle-llama-3-1-8b-unsloth-notebook), [Phi-4 (14B)](https://www.kaggle.com/code/danielhanchen/phi-4-finetuning-unsloth-notebook), [Mistral (7B)](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)

- See detailed documentation for Unsloth [here](https://docs.unsloth.ai/).



## ⚡ Quickstart



- **Install with pip (recommended)** for Linux devices:

```

pip install unsloth

```

For Windows install instructions, see [here](https://docs.unsloth.ai/get-started/installing-+-updating/windows-installation).



## 🦥 Unsloth.ai News

- 📣 NEW! **[Llama 4](https://unsloth.ai/blog/llama4)**, Meta's latest models including Scout & Maverick are now supported.

- 📣 NEW! [**EVERYTHING** is now supported](https://unsloth.ai/blog/gemma3#everything) incuding: FFT, ALL models (Mixtral, MOE, Cohere, Mamba) and all training algorithms (KTO, DoRA) etc. MultiGPU support coming very soon.

  To enable full-finetuning, set ```full_finetuning = True``` and for 8-bit finetuning, set ```load_in_8bit = True```

- 📣 NEW! **Gemma 3** by Google: [Read Blog](https://unsloth.ai/blog/gemma3). We [uploaded GGUFs, 4-bit models](https://huggingface.co/collections/unsloth/gemma-3-67d12b7e8816ec6efa7e4e5b).

- 📣 NEW! Introducing Long-context [Reasoning (GRPO)](https://unsloth.ai/blog/grpo) in Unsloth. Train your own reasoning model with just 5GB VRAM. Transform Llama, Phi, Mistral etc. into reasoning LLMs!

- 📣 NEW! [DeepSeek-R1](https://unsloth.ai/blog/deepseek-r1) - the most powerful open reasoning models with Llama & Qwen distillations. Run or fine-tune them now [with our guide](https://unsloth.ai/blog/deepseek-r1). All model uploads: [here](https://huggingface.co/collections/unsloth/deepseek-r1-all-versions-678e1c48f5d2fce87892ace5).

- 📣 NEW! [Phi-4](https://unsloth.ai/blog/phi4) by Microsoft: We also [fixed bugs](https://unsloth.ai/blog/phi4) in Phi-4 and [uploaded GGUFs, 4-bit](https://huggingface.co/collections/unsloth/phi-4-all-versions-677eecf93784e61afe762afa).

- 📣 Introducing Unsloth [Dynamic 4-bit Quantization](https://unsloth.ai/blog/dynamic-4bit)! 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.](https://huggingface.co/collections/unsloth/unsloth-4-bit-dynamic-quants-67503bb873f89e15276c44e7)

- 📣 [Vision models](https://unsloth.ai/blog/vision) now supported! [Llama 3.2 Vision (11B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb), [Qwen 2.5 VL (7B)](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2_VL_(7B)-Vision.ipynb) and [Pixtral (12B) 2409](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Pixtral_(12B)-Vision.ipynb)



  Click for more news



- 📣 [Llama 3.3 (70B)](https://huggingface.co/collections/unsloth/llama-33-all-versions-67535d7d994794b9d7cf5e9f), Meta's latest model is supported.

- 📣 We worked with Apple to add [Cut Cross Entropy](https://arxiv.org/abs/2411.09009). 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](https://unsloth.ai/blog/gradient)! Please update Unsloth and transformers.

- 📣 Try out [Chat interface](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Unsloth_Studio.ipynb)!

- 📣 NEW! Qwen-2.5 including [Coder](https://unsloth.ai/blog/qwen-coder) models are now supported with bugfixes. 14b fits in a Colab GPU! [Qwen 2.5 conversational notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_Coder_(14B)-Conversational.ipynb)

- 📣 NEW! [Mistral Small 22b notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_Small_(22B)-Alpaca.ipynb) finetuning fits in under 16GB of VRAM!

- 📣 NEW! `pip install unsloth` now works! Head over to [pypi](https://pypi.org/project/unsloth/) to check it out! This allows non git pull installs. Use `pip install unsloth[colab-new]` for non dependency installs.

- 📣 NEW! Continued Pretraining [notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-CPT.ipynb) for other languages like Korean!

- 📣 [2x faster inference](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Inference.ipynb) added for all our models

- 📣 We cut memory usage by a [further 30%](https://unsloth.ai/blog/long-context) and now support [4x longer context windows](https://unsloth.ai/blog/long-context)!



## 🔗 Links and Resources

| Type                            | Links                               |

| ------------------------------- | --------------------------------------- |

| 📚 **Documentation & Wiki**              | [Read Our Docs](https://docs.unsloth.ai) |

|   **Twitter (aka X)**              |  [Follow us on X](https://twitter.com/unslothai)|

| 💾 **Installation**               | [Pip install](https://docs.unsloth.ai/get-started/installing-+-updating)|

| 🔮 **Our Models**            | [Unsloth Releases](https://docs.unsloth.ai/get-started/all-our-models)|

| ✍️ **Blog**                    | [Read our Blogs](https://unsloth.ai/blog)|

|   **Reddit**                    | [Join our Reddit page](https://reddit.com/r/unsloth)|



## ⭐ Key Features

- Supports **full-finetuning**, pretraining, 4b-bit, 16-bit and **8-bit** training

- All kernels written in [OpenAI's Triton](https://openai.com/index/triton/) language. **Manual backprop engine**.

- **0% loss in accuracy** - no approximation methods - all exact.

- No change of hardware. Supports NVIDIA GPUs since 2018+. Minimum CUDA Capability 7.0 (V100, T4, Titan V, RTX 20, 30, 40x, A100, H100, L40 etc) [Check your GPU!](https://developer.nvidia.com/cuda-gpus) GTX 1070, 1080 works, but is slow.

- Works on **Linux** and **Windows**

- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).

- If you trained a model with 🦥Unsloth, you can use this cool sticker!   



## 💾 Install Unsloth

You can also see our documentation for more detailed installation and updating instructions [here](https://docs.unsloth.ai/get-started/installing-+-updating).



### Pip Installation

**Install with pip (recommended) for Linux devices:**

```

pip install unsloth

```

See [here](https://github.com/unslothai/unsloth/edit/main/README.md#advanced-pip-installation) for advanced pip install instructions.

### Windows Installation

> [!warning]

> Python 3.13 does not support Unsloth. Use 3.12, 3.11 or 3.10



1. **Install NVIDIA Video Driver:**

  You should install the latest version of your GPUs driver. Download drivers here: [NVIDIA GPU Drive](https://www.nvidia.com/Download/index.aspx).



3. **Install Visual Studio C++:**

   You will need Visual Studio, with C++ installed. By default, C++ is not installed with [Visual Studio](https://visualstudio.microsoft.com/vs/community/), 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](https://docs.unsloth.ai/get-started/installing-+-updating).



5. **Install CUDA Toolkit:**

   Follow the instructions to install [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit-archive).



6. **Install PyTorch:**

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

   [Install PyTorch](https://pytorch.org/get-started/locally/).



7. **Install Unsloth:**

   

```python

pip install unsloth

```



#### Notes

To run Unsloth directly on Windows:

- Install Triton from this Windows fork and follow the instructions [here](https://github.com/woct0rdho/triton-windows) (be aware that the Windows fork requires PyTorch >= 2.4 and CUDA 12)

- In the SFTTrainer, set `dataset_num_proc=1` to avoid a crashing issue:

```python

trainer = SFTTrainer(

    dataset_num_proc=1,

    ...

)

```



#### Advanced/Troubleshooting



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



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 installated correctly. Try `nvcc`. If that fails, you need to install `cudatoolkit` or CUDA drivers.

3. Install `xformers` manually. You can try installing `vllm` and seeing if `vllm` succeeds. 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.

4. Double check that your versions of Python, CUDA, CUDNN, `torch`, `triton`, and `xformers` are compatible with one another. The [PyTorch Compatibility Matrix](https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix) may be useful. 

5. 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`.

```bash

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

```



  If you're looking to install Conda in a Linux environment, read here, or run the below 🔽

  

  ```bash

  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:

```bash

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:

```bash

pip install --upgrade pip

pip install "unsloth[cu124-torch250] @ git+https://github.com/unslothai/unsloth.git"

```



And other examples:

```bash

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:

```bash

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

```



Or, run the below manually in a Python REPL:

```python

try: import torch

except: raise ImportError('Install torch via `pip install torch`')

from packaging.version import Version as V

v = V(torch.__version__)

cuda = str(torch.version.cuda)

is_ampere = torch.cuda.get_device_capability()[0] >= 8

if cuda != "12.1" and cuda != "11.8" and cuda != "12.4": 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('2.6.0'): x = 'cu{}{}-torch250'

else: raise RuntimeError(f"Torch = {v} too new!")

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"')

```



## 📜 Documentation

- Go to our official [Documentation](https://docs.unsloth.ai) for saving to GGUF, checkpointing, evaluation and more!

- We support Huggingface's TRL, Trainer, Seq2SeqTrainer or even Pytorch code!

- We're in 🤗Hugging Face's official docs! Check out the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!

- If you want to download models from the ModelScope community, please use an environment variable: `UNSLOTH_USE_MODELSCOPE=1`, and install the modelscope library by: `pip install modelscope -U`.



> unsloth_cli.py also supports `UNSLOTH_USE_MODELSCOPE=1` to download models and datasets. please remember to use the model and dataset id in the ModelScope community.



```python

from unsloth import FastLanguageModel 

import torch

from trl import SFTTrainer, SFTConfig

from datasets import load_dataset

max_seq_length = 2048 # Supports RoPE Scaling interally, 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(

        dataset_text_field = "text",

        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

```





## 💡 Reinforcement Learning

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](https://huggingface.co/learn/nlp-course/en/chapter12/6) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)! List of RL notebooks:



- ORPO notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-ORPO.ipynb)

- DPO Zephyr notebook: [Link](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Zephyr_(7B)-DPO.ipynb)

- KTO notebook: [Link](https://colab.research.google.com/drive/1MRgGtLWuZX4ypSfGguFgC-IblTvO2ivM?usp=sharing)

- SimPO notebook: [Link](https://colab.research.google.com/drive/1Hs5oQDovOay4mFA6Y9lQhVJ8TnbFLFh2?usp=sharing)



  Click for DPO code

  

```python

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()

```



## 🥇 Performance Benchmarking

- For our most detailed benchmarks, read our [Llama 3.3 Blog](https://unsloth.ai/blog/llama3-3).

- Benchmarking of Unsloth was also conducted by [🤗Hugging Face](https://huggingface.co/blog/unsloth-trl).



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                 |



### 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.



| GPU VRAM | 🦥Unsloth context length | Hugging Face + FA2 |

|----------|------------------------|------------------|

| 48 GB    | 12,106                | OOM              |

| 80 GB    | 89,389                | 6,916            |







![](https://i.ibb.co/sJ7RhGG/image-41.png)





### Citation



You can cite the Unsloth repo as follows:

```bibtex

@software{unsloth,

  author = {Daniel Han, Michael Han and Unsloth team},

  title = {Unsloth},

  url = {http://github.com/unslothai/unsloth},

  year = {2023}

}

```



### Thank You to

- Hugging Face's [TRL library](https://github.com/huggingface/trl) which serves as the basis foundation for Unsloth

- [Erik](https://github.com/erikwijmans) for his help adding [Apple's ML Cross Entropy](https://github.com/apple/ml-cross-entropy) in Unsloth

- [HuyNguyen-hust](https://github.com/HuyNguyen-hust) for making [RoPE Embeddings 28% faster](https://github.com/unslothai/unsloth/pull/238)

- [RandomInternetPreson](https://github.com/RandomInternetPreson) for confirming WSL support

- [152334H](https://github.com/152334H) for experimental DPO support