Fine-tuning Large Language Models

​Join us for an in-depth exploration of how and when to fine-tune large language models (LLMs) for optimal performance. This session features hands-on code demos and practical insights into working with diverse datasets. Whether you're looking to improve summarization, enable multi-turn conversations, or understand advanced techniques like loss masking, this webinar has you covered.

​What You’ll Learn:

• ​Fine-Tuning vs RAG: When to fine-tune your LLM and when Retrieval-Augmented Generation (RAG) is the better choice.

• ​Long Context Tasks: Best practices for fine-tuning LLMs to handle tasks like summarization or document analysis effectively.

• ​Loss Masking Demystified: How loss masking works and when it can boost your model’s performance.

• ​Multi-Turn Conversations: Techniques for fine-tuning with conversation datasets to build more interactive and contextual systems.

• ​Performance Insights: Real-world examples showcasing performance improvements across these tasks.

​This session is perfect for developers, researchers, and AI enthusiasts looking to elevate their LLM capabilities.