This article aims to illustrate how to fine-tune a top-performing LLM efficiently and cost-effectively on a custom dataset. We will explore the utilization of the Falcon-7B model with LoRA adapters using Lit-GPT.
Ever wondered what it would be like to have a digital twin? A virtual replica of yourself that can have conversations, learn, and even reflect your thoughts? Recent advances in artificial intelligence (AI) have made this once-futuristic idea attainable.
The AI community’s effort has led to the development of many high-quality open-source LLMs, including but not limited to Open LLaMA, Falcon, StableLM, and Pythia. You can fine-tune these models on a custom instruction dataset to adapt to your specific task, such as training a chatbot to answer financial questions. Furthermore, it can also provide a data privacy advantage when data cannot be uploaded or shared with cloud APIs.
In my case, I wanted the model to learn to speak my style by imitating me, using my jokes and filler words.
Data collection and preparation
Before we dive into the details, I’d like to point out that fine-tuning GPT-like models can be quite tricky. Nevertheless, I made the decision to take it a step further and train the model in the Russian language:
- This presents an additional challenge since models are primarily trained on English texts.
- Given that Russian is my native language, I possess a vast dataset comprising my personal correspondences.
Data collection
I chose Telegram because it provides a convenient API for data collection. Additionally, it serves as the primary platform for most of my communications with friends. This choice provides a valuable dataset that allows the model to gain a deeper understanding of my unique communication style and enables it to mimic me more effectively.