Is fine-tuning a better fit for your use case than Retrieval-Augmented Generation (RAG)? It might be! Let’s dive into some popular methods for fine-tuning large language models (LLMs).

Many hosted solutions, like OpenAI’s ChatGPT, allow you to fine-tune models by simply uploading a formatted dataset. The process is automatic, requiring minimal user interaction. OpenAI even provides a user-friendly interface for those who prefer not to use the API. However, this ease of use comes at the cost of control over the fine-tuning process.

If you’re working with publicly available foundation models, here are some flexible alternatives:

Recipe Scripts

Most foundation models, like Meta’s LLaMA, come with recipe scripts provided by the model creators. These scripts allow you to fine-tune models directly or serve as a great starting point for custom implementations.

PEFT (Parameter-Efficient Fine-Tuning)

Hugging Face offers PEFT, a method designed for efficient fine-tuning that integrates seamlessly with libraries like Transformers and Accelerate. Many LLaMA-based fine-tuning approaches use PEFT under the hood.

Torchtune

Torchtune offers native Pytorch implementations of popular LLMs and recipes for fine-tuning techniques like LoRA and QLoRA.

Axolotl

Axolotl simplifies fine-tuning through configuration files and terminal commands, streamlining the process for various AI models.

Unsloth

Unsloth specializes in fine-tuning public LLMs and claims to use significantly less memory while being faster than other solutions.

These are just a few of the available options, each with its own pros and cons. Some offer full control over the fine-tuning process, while others simplify it to a few adjustable parameters.

Have I missed any other fine-tuning methods or tools? Let’s discuss! 👇