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:
- How to build an instruction dataset
- How to fine-tune a Llama 2 model
- How to use and evaluate the trained model
Additional Resources:
Solution notebook (dataset): [ Ссылка ] /////
Solution Model: [ Ссылка ]
[SKILL TRACK] AI Fundamentals: [ Ссылка ]
[SKILL TRACK] AI Business Fundamentals: [ Ссылка ]
[BLOG] Introduction to Meta AI’s LLaMA: [ Ссылка ]
[BLOG] Fine-Tuning LLaMA 2: A Step-by-Step Guide to Customizing the Large Language Model: [ Ссылка ]
[BLOG] Llama.cpp Tutorial: A Complete Guide to Efficient LLM Inference and Implementation: [ Ссылка ]
