DataFramed

#229 Inside Meta's Biggest and Best Open-Source AI Model Yet with Thomas Scialom, Co-Creator of Llama3

Jul 25, 2024

Discover the groundbreaking advancements of Meta's Llama 3.1, touted as the largest open-source AI model yet. Delve into the complexities of training large language models, including the crucial role of hyperparameters and diverse data types. Explore the competitive landscape of open-source versus proprietary models and how they're shaping the future of AI. Experts discuss the potential breakthroughs in generative AI and the ongoing investments driving innovation. This is a riveting look into the constantly evolving world of AI!

39:23

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Quick takeaways

Llama 405B competes with top models like GPT-40, focusing on model advancements and open-source accessibility

Effective training of large language models necessitates optimal parameter selection and data cleansing for model efficiency

Deep dives

New Release of Meta's Larma 3.1 Large Language Models

Meta has introduced the latest Larma 3.1 family of large language models, Llama 405B, as the most extensive open source LLM available. This model is designed to compete against existing performance leaders like GPT-40, Claude, and Gemini, showcasing advancements in large language models over recent years.

Balance Exploration and Exploitation Wisely

01:21

No, AI development is not hitting a plateau

00:55

Progress Demands Self-Critique

01:16

Intro

2min

The Unveiling of Llama 405b: A Milestone in Open-Source AI

2min

Navigating the Challenges of Large Language Model Training

17min

Enhancing AI Performance Through Multimodal Data Training

2min

The Evolving Landscape of Deep Learning and Future Potential

2min

Dynamics of Language Models: Open Source vs. Proprietary

11min

Exploring the Future of AI: Trends and Breakthroughs

3min

Meta has been at the absolute edge of the open-source AI ecosystem, and with the recent release of Llama 3.1, they have officially created the largest open-source model to date. So, what's the secret behind the performance gains of Llama 3.1? What will the future of open-source AI look like?

Thomas Scialom is a Senior Staff Research Scientist (LLMs) at Meta AI, and is one of the co-creators of the Llama family of models. Prior to joining Meta, Thomas worked as a Teacher, Lecturer, Speaker and Quant Trading Researcher.

In the episode, Adel and Thomas explore Llama 405B it’s new features and improved performance, the challenges in training LLMs, best practices for training LLMs, pre and post-training processes, the future of LLMs and AI, open vs closed-sources models, the GenAI landscape, scalability of AI models, current research and future trends and much more.

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DataFramed

#229 Inside Meta's Biggest and Best Open-Source AI Model Yet with Thomas Scialom, Co-Creator of Llama3

Podcast summary created with Snipd AI

Quick takeaways

Deep dives

New Release of Meta's Larma 3.1 Large Language Models

Challenges in Training Large Language Models

Ensuring High-Quality Data for Model Training

Future Trends in Generative AI and Large Language Models

Balance Exploration and Exploitation Wisely

No, AI development is not hitting a plateau

Progress Demands Self-Critique

Remember Everything You Learn from Podcasts