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### Finetune Llama 3.1, Mistral, Phi-3 & Gemma 2-5x faster with 80% less memory!
</div>
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, Ollama, vLLM or uploaded to Hugging Face.
- Run [Llama 3 conversational notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) and [Mistral v0.3 ChatML](https://colab.research.google.com/drive/15F1xyn8497_dUbxZP4zWmPZ3PJx1Oymv?usp=sharing)
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for continued pretraining / raw text
- This [continued pretraining notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) is for learning another language
- Click [here](https://github.com/unslothai/unsloth/wiki) for detailed documentation for Unsloth.
## 🦥 Unsloth.ai News
- 📣 NEW! [Llama 3.1 8b, 70b](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) both Base and Instruct now supported
- 📣 NEW! [Mistral Nemo-12b](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing) both Base and Instruct now supported
- 📣 NEW! [Gemma-2-9b](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) and Gemma-2-27b now supported
- 📣 NEW! Continued Pretraining [notebook](https://colab.research.google.com/drive/1tEd1FrOXWMnCU9UIvdYhs61tkxdMuKZu?usp=sharing) for other languages like Korean!
- 📣 NEW! Qwen2 now works
- 📣 [Mistral v0.3 Base](https://colab.research.google.com/drive/1_yNCks4BTD5zOnjozppphh5GzMFaMKq_?usp=sharing) and [Mistral v0.3 Instruct]
- 📣 [ORPO support](https://colab.research.google.com/drive/11t4njE3c4Lxl-07OD8lJSMKkfyJml3Tn?usp=sharing) is here + [2x faster inference](https://colab.research.google.com/drive/1aqlNQi7MMJbynFDyOQteD2t0yVfjb9Zh?usp=sharing) 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)!
| <imgheight="14"src="https://upload.wikimedia.org/wikipedia/commons/6/6f/Logo_of_Twitter.svg"/> **Twitter (aka X)** | [Follow us on X](https://twitter.com/unslothai)|
- All kernels written in [OpenAI's Triton](https://openai.com/research/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** via WSL.
- Supports 4bit and 16bit QLoRA / LoRA finetuning via [bitsandbytes](https://github.com/TimDettmers/bitsandbytes).
- Open source trains 5x faster - see [Unsloth Pro](https://unsloth.ai/) for up to **30x faster training**!
- If you trained a model with 🦥Unsloth, you can use this cool sticker! <imgsrc="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png"height="50"align="center"/>
## 🥇 Performance Benchmarking
- For the full list of **reproducible** benchmarking tables, [go to our website](https://unsloth.ai/blog/mistral-benchmark#Benchmark%20tables)
| 1 A100 40GB | 🤗Hugging Face | Flash Attention | 🦥Unsloth Open Source | 🦥[Unsloth Pro](https://unsloth.ai/pricing) |
Select either `pytorch-cuda=11.8` for CUDA 11.8 or `pytorch-cuda=12.1` for CUDA 12.1. If you have `mamba`, use `mamba` instead of `conda` for faster solving. See this [Github issue](https://github.com/unslothai/unsloth/issues/73) for help on debugging Conda installs.
Do **NOT** use this if you have Anaconda. You must use the Conda install method, or else stuff will BREAK.
1. Find your CUDA version via
```python
importtorch;torch.version.cuda
```
2. For Pytorch 2.1.0: You can update Pytorch via Pip (interchange `cu121` / `cu118`). Go to https://pytorch.org/ to learn more. Select either `cu118` for CUDA 11.8 or `cu121` for CUDA 12.1. If you have a RTX 3060 or higher (A100, H100 etc), use the `"ampere"` path. For Pytorch 2.1.1: go to step 3. For Pytorch 2.2.0: go to step 4.
8. To troubleshoot installs try the below (all must succeed). Xformers should mostly all be available.
```bash
nvcc
python -m xformers.info
python -m bitsandbytes
```
## 📜 Documentation
- Go to our [Wiki page](https://github.com/unslothai/unsloth/wiki) 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)!
```python
fromunslothimportFastLanguageModel
fromunslothimportis_bfloat16_supported
importtorch
fromtrlimportSFTTrainer
fromtransformersimportTrainingArguments
fromdatasetsimportload_dataset
max_seq_length=2048# Supports RoPE Scaling interally, so choose any!
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,
dataset_text_field="text",
max_seq_length=max_seq_length,
tokenizer=tokenizer,
args=TrainingArguments(
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
warmup_steps=10,
max_steps=60,
fp16=notis_bfloat16_supported(),
bf16=is_bfloat16_supported(),
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
```
<aname="DPO"></a>
## DPO Support
DPO (Direct Preference Optimization), PPO, Reward Modelling all seem to work as per 3rd party independent testing from [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory). We have a preliminary Google Colab notebook for reproducing Zephyr on Tesla T4 here: [notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing).
We're in 🤗Hugging Face's official docs! We're on the [SFT docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth) and the [DPO docs](https://huggingface.co/docs/trl/main/en/dpo_trainer#accelerate-dpo-fine-tuning-using-unsloth)!
