113 citations · 1k across the 66 of their papers we have counts for
9 papers · 1 filter
Weight subcloning: direct initialization of transformers using larger pretrained ones
Mohammad Samragh, Mehrdad Farajtabar, Sachin Mehta +5
Training large transformer models from scratch for a target task requires lots of data and is computationally demanding. The usual practice of transfer learning overcomes this chal…
LLM in a flash: Efficient Large Language Model Inference with Limited Memory
Keivan Alizadeh, Iman Mirzadeh, Dmitry Belenko +5
Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and me…
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific Models
Raviteja Vemulapalli, Hadi Pouransari, Fartash Faghri +4
Vision Foundation Models (VFMs) pretrained on massive datasets exhibit impressive performance on various downstream tasks, especially with limited labeled target data. However, due…
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement
Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton +7
Contrastive language image pretraining (CLIP) is a standard method for training vision-language models. While CLIP is scalable, promptable, and robust to distribution shifts on ima…
ReLU Strikes Back: Exploiting Activation Sparsity in Large Language Models
Iman Mirzadeh, Keivan Alizadeh, Sachin Mehta +5
Large Language Models (LLMs) with billions of parameters have drastically transformed AI applications. However, their demanding computation during inference has raised significant…
TiC-CLIP: Continual Training of CLIP Models
Saurabh Garg, Mehrdad Farajtabar, Hadi Pouransari +5
Keeping large foundation models up to date on latest data is inherently expensive. To avoid the prohibitive costs of constantly retraining, it is imperative to continually train th…