Train and Serve an AI Chatbot Based on Llama 3.2

Efficient supervised fine-tuning with TRL

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Creating a custom AI chatbot is now more accessible and cost-effective than ever. With parameter-efficient fine-tuning methods like LoRA and QLoRA, even small but powerful models like Llama 3.2 can be adapted to train high-quality chatbots for just a few dollars.

In this article, we will see how to format a question-answering dataset and fine-tune Llama 3.2 using supervised fine-tuning (SFT). We'll highlight common pitfalls to avoid during SFT and demonstrate how to train a chatbot with a synthetic dataset.

Generate Synthetic Data from Personas to Train AI Chatbots

Each of these steps is explained in detail within the article and implemented in this notebook:

Formatting a Synthetic Dataset of Questions and Answers for Supervised Fine-tuning