Sharing my walkthrough on fine-tuning LLMs with LoRA using NVIDIA's NeMo microservices. The result is a llama-3.2-1b-instruct model fine-tuned to be really good at function-calling, making it ideal for agent-use.
It was a ton of fun to figure it out and it brought back some nostalgia from the days of training ML models, tweaking learning rates, dropout, and watching loss charts in W&B.
Final performance was way better than any 1-3B parameter LLM I tried with agentic workflows in the past.
It was a ton of fun to figure it out and it brought back some nostalgia from the days of training ML models, tweaking learning rates, dropout, and watching loss charts in W&B.
Final performance was way better than any 1-3B parameter LLM I tried with agentic workflows in the past.