2 citations · 2 across the 10 of their papers we have counts for
10 papers
Thinking at the Right Size: Amortized Distillation Across Post-Trained LLMs
Yan Zhou, Sara Kangaslahti, Jonathan Geuter +4
Practical deployment of large language models (LLMs) requires families of post-trained variants---instruction-tuned, reasoning-tuned, and chat-style models---each at multiple sizes…
Understanding Layer Patching in Model Size Interpolation
Sara Kangaslahti, Jonathan Geuter, Nihal V. Nayak +3
Zero-shot model size interpolation aims to create new models of intermediate target sizes by combining existing models without additional training. Recent work on boomerang distill…
GenAI for Systems: Recurring Challenges and Design Principles from Software to Silicon
Arya Tschand, Chenyu Wang, Zishen Wan +21
Generative AI is reshaping how computing systems are designed, optimized, and built, yet research remains fragmented across software, architecture, and chip design communities. Thi…
Inverse Depth Scaling From Most Layers Being Similar
Yizhou Liu, Sara Kangaslahti, Ziming Liu +1
Neural scaling laws relate loss to model size in large language models (LLMs), yet depth and width may contribute to performance differently, requiring more detailed studies. Here,…
Analyzing Political Text at Scale with Online Tensor LDA
Sara Kangaslahti, Danny Ebanks, Jean Kossaifi +3
This paper proposes a topic modeling method that scales linearly to billions of documents. We make three core contributions: i) we present a topic modeling method, Tensor Latent Di…
Boomerang Distillation Enables Zero-Shot Model Size Interpolation
Sara Kangaslahti, Nihal V. Nayak, Jonathan Geuter +3
Large language models (LLMs) are typically deployed under diverse memory and compute constraints. Existing approaches build model families by training each size independently, whic…