
Gorilla: Large Language Model Connected with Massive APIs
This talk will introduce the Gorilla project, designed to connect LLMs with various services and applications exposed through APIs.
This talk will introduce the Gorilla project, designed to connect LLMs with various services and applications exposed through APIs.
This overview talk will be about sports analytics and using AI/ML for player performance, business operations, and recruitment statistics.
This talk will explore potential risks, break down advanced defense methods, and share the latest on keeping Large Language Models safe.
This talk will share some reference architectures and demos for emerging Generative AI applications and use cases.
This talk will discuss foundation and ChatGPT-style models, GenAI and LLM technology at NVIDIA, shortcomings, and proposed guardrails.
During the talk, we will review the existing tools and see how to move from development to production without a headache.
This talk will dive into “data frameworks” like LlamaIndex, and demonstrate how to create hierarchical indexes from different data sources.
This talk will discuss synthetic data, some impactful use cases associated with it, and the challenges companies face while harnessing it.
This talk will discuss why multilingual semantic search is amazing, how respective models are trained, and the new use cases this unlocks.
This talk will go into the intricacies of assessing the quality of LLMs and best practices to ensure their reliability and accuracy.
This talk will discuss ways to reduce costs for NLP inference through a better choice of model, hardware, and model compression techniques.
This talk will discuss how businesses can leverage FMs using Prompt Engineering and build Generative AI applications in the cloud.