Dive into an hour-long interview with Thomas Scialom, the lead of LLaMA 2 at Meta, as he shares insights on his career journey and contributions to major AI projects. Explore topics including the transformative potential of large language models, the importance of supervised fine-tuning, and the role of human preference in AI development. Gain valuable perspectives on the future of AI technology and participate in a Q&A session covering various aspects of language models and their applications.
LLaMA 2 and Meta AI Projects - Interview with Thomas Scialom
Aleksa Gordić - The AI Epiphany via YouTube
Overview
Syllabus
Thomas''s story
The Sci-Fi moment
Large is all you need
Supervised fine-tuning
Human Preference
What happens next?
Q&A
Taught by
Aleksa Gordić - The AI Epiphany
Related Courses
-
Embeddings vs Fine-Tuning - Supervised Fine-tuning - Part 2
-
Fine-Tuning and Deploying LLaMA 3.1 on Hugging Face and Ollama
-
LLM News: Llama 3.1, Self-Improving Agents, and Recent AI Developments
-
How to Build a Q&A AI in Python - Open-Domain Question-Answering
-
Enabling LLM-Powered Applications with Harrison Chase of LangChain
