GitHub

Unsloth

What is the project about?

Unsloth is a project focused on accelerating the fine-tuning of Large Language Models (LLMs) like Llama 3, Mistral, Phi-4, Qwen 2.5, and Gemma. It achieves this through optimized kernels written in OpenAI's Triton language, providing significant speedups and reduced memory usage compared to standard methods.

What problem does it solve?

Fine-tuning LLMs is typically resource-intensive, requiring substantial GPU memory and time. Unsloth addresses this by:

  • Reducing training time: It significantly speeds up the fine-tuning process (up to 2x faster, with claims of up to 30x faster in the Pro version).
  • Lowering memory requirements: It drastically reduces the GPU memory needed for fine-tuning (up to 80% less), making it accessible to users with less powerful hardware.
  • Enabling longer context windows: It allows for much longer context lengths during fine-tuning, surpassing the native capabilities of the base models.
  • Fixing bugs: It addresses bugs in some models, such as Phi-4.

What are the features of the project?

  • Triton Kernels: Custom kernels written in OpenAI's Triton for optimized performance.
  • Manual Backpropagation: A manual backpropagation engine for greater control and efficiency.
  • No Accuracy Loss: Maintains full precision without approximation methods.
  • Wide Hardware Support: Compatible with NVIDIA GPUs from 2018 onwards (CUDA Capability 7.0+).
  • OS Compatibility: Works on Linux and Windows (via WSL).
  • 4-bit and 16-bit Support: Supports QLoRA and LoRA fine-tuning in both 4-bit and 16-bit modes.
  • Easy to use Colab Notebooks: Beginner-friendly notebooks for various models.
  • Dynamic 4-bit Quantization: Improves accuracy with minimal VRAM increase.
  • Vision Models Support: Fine-tuning for vision language models.
  • DPO Support: Supports Direct Preference Optimization.
  • Long Context Support: Enables significantly longer context windows than base models.
  • GRPO (R1 Reasoning): Supports the reproduction of DeepSeek-R1's reasoning capabilities.
  • Continued Pretraining: Supports continued pretraining for learning other languages.
  • Hugging Face Integration: Integrated with Hugging Face's TRL and Trainer.

What are the technologies used in the project?

  • Triton: OpenAI's language for writing GPU kernels.
  • PyTorch: The underlying deep learning framework.
  • CUDA: NVIDIA's parallel computing platform.
  • LoRA/QLoRA: Parameter-efficient fine-tuning techniques.
  • bitsandbytes: A library for quantization.
  • Hugging Face Transformers: A library for working with pre-trained models.
  • TRL (Transformer Reinforcement Learning): A library for reinforcement learning with transformers.
  • xFormers / Flash Attention: For memory-efficient attention mechanisms.

What are the benefits of the project?

  • Faster Fine-tuning: Significant speed improvements reduce training time.
  • Reduced Memory Usage: Lower GPU memory requirements make fine-tuning accessible to more users.
  • Cost Savings: Less time and resources needed for training translate to lower costs.
  • Accessibility: Enables fine-tuning on less powerful hardware.
  • Longer Context: Allows for training on longer sequences.
  • Easy to Use: Beginner-friendly notebooks simplify the process.

What are the use cases of the project?

  • Customizing LLMs: Fine-tuning pre-trained models for specific tasks or domains.
  • Research: Experimenting with LLMs and fine-tuning techniques.
  • Development: Building applications that require customized language models.
  • Resource-Constrained Environments: Fine-tuning LLMs when access to high-end GPUs is limited.
  • Learning New Languages: Continued pretraining for adapting models to different languages.
  • Vision-Language Tasks: Fine-tuning vision-language models for tasks involving images and text.
  • Preference Optimization: Using DPO to align models with human preferences.
  • Reasoning Tasks: Fine-tuning models for improved reasoning capabilities.
unsloth screenshot