Vimal Thilak

Today we have released the code and a demo iOS application for FastVLM - our extremely efficient and fast vision language model which runs on your device using MLX! You can check out the code and the app here: github.com/apple/ml-fas...

#ICLR #TrainBetterLM I am at ICLR, come to our posters for improved language model training!

Recycle gradients for faster neural net training with AdEMAmix iclr.cc/virtual/2025... (Fri Apr 25, 10 am).

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Check our Pau and his Apple MLR team's blogpost on activation transport! Soon to be featured as a spotlight at ICLR :)

More scaling laws? Mustafa and his team at Apple MLR have you covered at least when ti comes to native multimodal models scaling laws :)

Calling all SSL practitioners -- check out this library done by the amazing \alpha-Req crew

Paper🧵 (cross-posted at X): When does composition of diffusion models "work"? Intuitively, the reason dog+hat works and dog+horse doesn’t has something to do with independence between the concepts being composed. The tricky part is to formalize exactly what this means. 1/

Excited to share Soup-of-Experts, a new neural network architecture that, for any given specific task, can instantiate in a flash a small model that is very good on it.

Made with ❤️ at Apple

Thanks to my co-authors David Grangier, Angelos Katharopoulos, and Skyler Seto!

arxiv.org/abs/2502.01804

🚨 Apple Machine Learning Research Internship opportunity! My colleagues in Apple MLR are looking for a PhD research intern with a strong interest in reinforcement learning/post-training for LLMs. If interested, apply by sending an email to Etai Littwin (elittwin at apple dot com)

Mixture of experts is an interesting architecture or so @samiraabnar.bsky.social told me when I joined the project last year. After some brilliant work from @harshay-shah.bsky.social and @samiraabnar.bsky.social , we have a scaling law paper!