Fine-Tuning and Aligning LLMs with Enterprise Data

LLMs often require fine-tuning and alignment on domain-specific knowledge before they can accurately, and reliably, perform specialized tasks within the enterprise.

The key to transforming foundation models such as Meta’s Llama 3 into specialized LLMs is high-quality training data which can be applied via fine-tuning and alignment.

In this session, we’ll provide an overview of methods such as SFT and DPO, show how to curate high-quality instruction and preference data 10-100x faster (and at scale) and demonstrate how to fine-tune, align and evaluate an LLM.

Join us, and learn more about:

• Curating high-quality training data 10-100x faster
• Emerging LLM fine-tuning and alignment methods 
• Evaluating LLM accuracy for production deployment