Fine-Tuning Your Own Llama 2 Model

The advent of large language models has taken the AI world by storm. Outside of proprietary foundation models like GPT-4, open-source models are playing a pivotal role in driving the AI revolution forward, democratizing access for anyone looking to leverage these models in production. One of the biggest challenges in generating high-quality output from open-source models rests in fine-tuning, where we improve their outputs based on a series of instructions.

In this session, we take a step-by-step approach to fine-tune a Llama 2 model on a custom dataset. First, we build our own dataset using techniques to remove duplicates and analyze the number of tokens. Then, we fine-tune the Llama 2 model using state-of-the art techniques from the Axolotl library. Finally, we see how to run our fine-tuned model and evaluate its performance.

Key Takeaways: