Fine-Tuning Your Own Llama 2 Model

In this session, we take a step-by-step approach to fine-tune a Llama 2 model on a custom dataset.

17 nov. 2023

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

Note: To participate in this code-along, you will need to have a valid Google Colab account. Get started here.

Additional Resources

Solution Notebook (dataset)

Solution Model

[SKILL TRACK] AI Fundamentals

[BLOG] Introduction to Meta AI’s LLaMA

[BLOG] Fine-Tuning LLaMA 2: A Step-by-Step Guide to Customizing the Large Language Model

[BLOG] Llama.cpp Tutorial: A Complete Guide to Efficient LLM Inference and Implementation

Sujets

Artificial Intelligence

Python

Deep Learning

Apparenté

didacticiel

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code-along

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