6 papers
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
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…
Bytes Are All You Need: Transformers Operating Directly On File Bytes
Maxwell Horton, Sachin Mehta, Ali Farhadi +1
Modern deep learning approaches usually utilize modality-specific processing. For example, the most common deep learning approach to image classification involves decoding image fi…
SAM-CLIP: Merging Vision Foundation Models towards Semantic and Spatial Understanding
Haoxiang Wang, Pavan Kumar Anasosalu Vasu, Fartash Faghri +6
The landscape of publicly available vision foundation models (VFMs), such as CLIP and Segment Anything Model (SAM), is expanding rapidly. VFMs are endowed with distinct capabilitie…
KV-Runahead: Scalable Causal LLM Inference by Parallel Key-Value Cache Generation
Minsik Cho, Mohammad Rastegari, Devang Naik
Large Language Model or LLM inference has two phases, the prompt (or prefill) phase to output the first token and the extension (or decoding) phase to the generate subsequent token…