Fine Tuning and Evaluating LLMs for Agentic Use Cases

Webinar on May 29, 10am PT

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 topics, enabled by Weights & Biases, including:

  • Fine-tuning techniques: Learn about LORA (low-rank adaptation) and its role in refining LLM behavior for specific applications for both open source (Llama2) and closed source models (GPT 3.5-turbo)
  • Metrics and logging: Understand the importance of tracking the right metrics and maintaining detailed logs as it relates to Language Models;
  • Model Checkpointing and Comparison: Implement systematic model checkpointing for tracking progress and comparing iterations, facilitating optimal version selection through detailed performance analysis.
  • Practical evaluation: Engage in hands-on evaluations to assess the improvements in your fine-tuned models, both quantitatively and qualitatively.
Anish Shah

Anish Shah

ML Engineer
Weights & Biases

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