This session is focused on fine-tuning large language model (LLM) agents, acquiring crucial insights and techniques for enhancing the performance and specificity of local LLM agents in application automation. We'll explore a variety of essential topics, including:
• Fine-tuning techniques: Learn about LORA (low-rank adaptation) and its role in refining LLM behavior for specific applications; • Metrics and logging: Understand the importance of tracking the right metrics and maintaining detailed logs using MLOps platforms like Weights & Biases (W&B); • Debugging with W&B: Discover how W&B Traces can be utilized for debugging and improving agent applications; • Prompting paradigms: Delve into various prompting styles and their impact on the agent's behavior and task performance; and • Practical evaluation with W&B: Engage in hands-on evaluations using Weights & Biases to assess the improvements in your fine-tuned models, both quantitatively and qualitatively.