Create a Clone of Yourself With a Fine-tuned LLM

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 <a href="https://github.com/Lightning-AI/lit-gpt" rel="noopener ugc nofollow" target="_blank">Lit-GPT</a>. 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. <h1>Data collection and preparation</h1> 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: <a href="https://betterprogramming.pub/unleash-your-digital-twin-how-fine-tuning-llm-can-create-your-perfect-doppelganger-b5913e7dda2e">Click Here</a>