🚀 Exciting News! 🚀 In a joint effort between IBM Research, Princeton, CMU, and UIUC, we are thrilled to announce the release of our high-performing hybrid Mamba2 model! This model is trained entirely on open datasets, and we’re releasing intermediate and final checkpoints to

.Red Hat AI Innovation team just dropped a new research paper on inference-time scaling! 🚨 All built on vLLM. Paper and code here: …abilistic-inference-scaling.github.io Cheers to paper authors Akash Srivastava, Kai Xu, GX Xu, Shivchander Sudalairaj, and Isha Puri!

[1/x] can we scale small, open LMs to o1 level? Using classical probabilistic inference methods, YES! Joint MIT CSAIL / Red Hat AI Innovation Team work introduces a particle filtering approach to scaling inference w/o any training! check out …abilistic-inference-scaling.github.io

Can we use classical probabilistic inference methods to scale small LMs to o1 level? 🤔 MIT CSAIL and Red Hat AI Innovation teams explore: bit.ly/3CHs1Zz

our new work w awesome Red Hat collaborators on novel inference scaling techniques - check out bit.ly/3CHs1Zz for more on how to scale small LMs to o1 performance! 🚀🚀🚀