This article discusses the process of fine-tuning a top-performing LLM (Large Language Model) on a custom dataset, specifically using the Falcon-7B model with LoRA adapters. The concept of creating a digital twin, which is a virtual replica of oneself, is introduced, and recent advancements in AI that make it attainable are highlighted. The article emphasizes the benefits of fine-tuning LLMs, including data privacy advantages and adaptability to specific tasks. The author shares their personal experience of collecting and preparing data, focusing on a dataset from their personal correspondences on the Telegram platform. The process of fine-tuning the Falcon model using the Lit-GPT library is explained, along with the use of LoRA for parameter-efficient fine-tuning. The article concludes with observations on model performance, limitations, and recommendations for achieving optimal results.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Clone Yourself with LLM | Creation of digital twin | More advanced and realistic digital twins | Advancements in AI and fine-tuning techniques |
| Fine-tuning LLMs on custom datasets | Fine-tuning models for specific tasks | More efficient and effective fine-tuning on personalized datasets | Need for personalized AI models and data privacy |
| Data collection and preparation | Collecting and processing data for fine-tuning | Improved data collection and processing techniques | Need for high-quality and relevant training data |
| Parameter-efficient LLM fine-tuning with LoRA | Enhanced fine-tuning with LoRA method | Faster and more resource-efficient fine-tuning | Optimization of training process and resource usage |
| Running inference with fine-tuned model | Generating text with fine-tuned LLMs | Faster and more accurate text generation | Improved text generation capabilities |
| Quality Comparison of fine-tuned models | Evaluating performance of fine-tuned models | Improved model performance through data enhancements and adjustments | Iterative improvements and optimizations in fine-tuning process |
| Limitations of using Lit-GPT for production | Challenges and limitations of using Lit-GPT in production | Development of alternative solutions for production use | Need for more robust and scalable LLM frameworks |
| Conclusion on fine-tuning LLMs | Impressive capabilities of fine-tuning LLMs | Increased utilization and optimization of fine-tuning techniques | Advancements in LLM research and applications |