84. Fine-Tuning LLMs: Teaching Giants New Tricks

GPT-3 has 175 billion parameters. Full fine-tuning updates all 175 billion with every gradient step. You need multiple A100 GPUs (each with 80GB memory) just to fit the model. Training for even a few epochs on a moderate dataset costs thousands of dollars. A startup cannot do this. A PhD student cannot do this. Yet fine-tuned versions of large models consistently outperform their base versions on specific tasks. The performance benefit is real. The cost is prohibitive. LoRA (Low-Rank Adaptati...