/ Open vs Closed Source Dilemma

Description

This talk explores the quality gap between open-source and closed-source large language models (LLMs). While closed-source models often outperform in terms of raw capabilities, open-source models can bridge this gap through effective fine-tuning. We'll discuss strategies for leveraging fine-tuning to enhance open-source LLM performance, making them competitive alternatives. Discover how to harness the power of customization to meet specific use-case needs.

Session 🗣 Intermediate ⭐⭐ Track: AI, ML, Bigdata, Python