Description
As AI becomes more crucial in our daily lives, transparency in AI models is vital. However, many Large Language Model (LLM) systems keep crucial information to train large language models at scale proprietary, contradicting the principles of openness that define the Python ecosystem.
This session invites you to explore how Snowflake's AI research team implements openness in building and sharing generative AI developments, prioritizing simplicity and ease-of-use while maintaining efficiency at scale.
In this talk, you’ll learn:
- How open tools, practices, datasets, and recipes contribute to and inform our research
- The recipes we used – including what worked and what didn’t – when training LLMs
- Developer-first example use cases of prompting, retrieval, and presentation using the datasets, embeddings, and model trainings we developed