1 citations · 1 across the 4 of their papers we have counts for
4 papers
In-Place Tokenizer Expansion for Pre-trained LLMs
Jimmy T. H. Smith, Tarek Dakhran, Alberto Cabrera +7
A tokenizer fixed at the start of pre-training allocates vocabulary in proportion to the pre-training corpus, reflecting the deployment priorities at that time. When those prioriti…
Zero-Overhead Introspection for Adaptive Test-Time Compute
Rohin Manvi, Joey Hong, Tim Seyde +3
Large language models excel at reasoning but lack key aspects of introspection, including anticipating their own success and the computation required to achieve it. Humans use real…
LFM2 Technical Report
Alexander Amini, Anna Banaszak, Harold Benoit +30
We present LFM2, a family of Liquid Foundation Models designed for efficient on-device deployment and strong task capabilities. Using hardware-in-the-loop architecture search under…
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
Thomas Gauthier-Caron, Shamane Siriwardhana, Elliot Stein +5
By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retrai…