1 citations · 1 across the 4 of their papers we have counts for
4 papers
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
Hanxian Huang, Igor Fedorov, Andrey Gromov +14
Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce nea…
Short Data, Long Context: Distilling Positional Knowledge in Transformers
Patrick Huber, Ernie Chang, Chinnadhurai Sankar +4
Extending the context window of language models typically requires expensive long-context pre-training, posing significant challenges for both training efficiency and data collecti…
CoDi: Conversational Distillation for Grounded Question Answering
Patrick Huber, Arash Einolghozati, Rylan Conway +6
Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in t…
PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
Charlie Hou, Akshat Shrivastava, Hongyuan Zhan +5
On-device training is currently the most common approach for training machine learning (ML) models on private, distributed user data. Despite this, on-device training has several d…