How to fine-tune a model?
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As large language models (LLMs) become more accessible and widely adopted, fine-tuning them on domain-specific data is now easier than ever. Whether you're working with LLAMA 3, Mistral, or Pythia, there are several modern open-source frameworks tailored to streamline the customization process.
Let’s take a look at three standout frameworks that have gained popularity for their simplicity, performance, and flexibility: Axolotl, Unsloth, and Torchtune.
Axolotl is a user-friendly framework built on top of Hugging Face's ecosystem. It simplifies the training loop and provides efficient defaults, sample packing, and helpful utilities that make it perfect for first-time fine-tuners. You don’t need to write your own scripts or dive deep into configuration files — just bring your dataset.
Why use it?
Great for beginners
Supports multi-GPU setups
Ideal for working with Hugging Face-hosted models
Unsloth is designed for those with limited GPU power. Created by a former Nvidia engineer, this framework dramatically cuts down on memory consumption and training time. It uses a custom Triton-powered attention kernel to provide blazing-fast training speeds, even on older GPUs.
What sets it apart?
Uses 2-5x less memory than traditional methods
Up to 5x faster fine-tuning
Perfect for setups like Google Colab's free tier
For users who want full control and pure PyTorch, Torchtune delivers. This lightweight framework comes without heavy abstractions, offering native compatibility with PyTorch and efficient recipes for parameter-efficient fine-tuning strategies like LoRA and qLoRA.
Key benefits:
Abstraction-free, PyTorch-native
Works well with consumer-grade GPUs
Highly modular and customizable
New to fine-tuning? Go with Axolotl.
Running low on GPU resources? Try Unsloth.
Need flexibility with PyTorch? Choose Torchtune.
Each framework fits a different niche and choosing the right one depends on your specific environment and goals. Whether you're just getting started or looking to optimize an existing pipeline, these tools make it easy to take pre-trained models and make them your own.
To fine-tune using Zeupiter on H100s, use the starter code here.
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