Maxime Labonne

Maxime Labonne, Head of Post Training at Liquid AI, and Elie Bakouch from Hugging Face, wade into the riveting world of AI advancements. They discuss the launch of Grok 4, a groundbreaking model that sets new benchmarks. Elie shares insights on SmolLM3, an open-source marvel that performs complex reasoning. The dynamic duo also highlights the rise of open-source tools, including Reka Flash, and the innovative Perplexity Comet browser. Join their conversation on the future of AI, its challenges, and how it's reshaping the tech landscape!

Maxime Labonne, a Senior Staff Machine Learning Scientist at Liquid AI, and Paul-Emil Iusztin, founder of Decoding ML, delve into the fascinating world of AI application development. They tackle the complexities of deploying AI models, from understanding fine-tuning nuances to the critical RAG feature pipeline. Strategies for effective problem definition and the importance of scalable solutions are highlighted. The discussion also covers managing deployment costs and the balance between safety and model customization, making the technical accessible and engaging.

Maxime Labonne, Head of Post-Training at Liquid AI and author of the LLM Engineers Handbook, dives into the future of AI design. He emphasizes that growth is achieved not just through bigger models, but through smarter, more efficient ones. Maxime explores the critical role of data quality, suggesting accuracy and diversity are key. He also discusses the challenges of deploying AI on edge devices and critiques the current hype surrounding AI technologies, advocating for a more nuanced understanding of their capabilities.

Dans ce podcast, Maxime Labonne, Head of Post-Training chez Liquid AI, évoque son parcours impressionnant, de la cybersécurité aux modèles de langage avancés. Il explique les défis du post-training et le fine-tuning supervisé, tout en présentant les Liquid Foundation Models qui transforment l'IA. Maxime parle aussi des innovations en matière d'architectures, des enjeux des workflows de développement assistés par l'IA et l'importance de l'open-source pour l'innovation en intelligence artificielle.

Maxime Labonne, a researcher at Liquid AI and creator of open-source models on Hugging Face, dives into the fascinating world of model merging. He reveals how averaging the weights of different models can lead to surprising performance gains. Originating from cybersecurity, Maxime discusses the concept of 'Frankenstein models' that thrive without expensive hardware. He also tackles the rising importance of synthetic data, the challenges of automated benchmarking, and cultural insights from the European tech scene. Tune in for innovative AI strategies!