In deploying powerful language models like GPT-3 for real-time applications, developers often need high latency, large memory footprints, and limited portability across diverse devices and operating systems.  Many need help with the complexities of integrating giant...

Commonsense reasoning is an essential facet of human cognition that enables intuitive interpretation and interaction with the world. In NLP, this translates into the ability of LLMs and Multimodal Large Language Models (MLLMs) to interpret human language and visual...

The challenge of creating adaptable and versatile visual assistants has become increasingly evident in the rapidly evolving Artificial Intelligence. Traditional models often grapple with fixed capabilities and need help to learn dynamically from diverse examples. The...

With the widespread adoption of Large Language Models (LLMs), the quest for efficient ways to run these models on consumer hardware has gained prominence. One promising strategy involves using sparse mixture-of-experts (MoE) architectures, where only selected model...

LLMs represent a significant leap in understanding and generating human language. These models are instrumental in various AI applications, from automated translation to conversational agents. Their development involves a delicate balance between enhancing...

In the domain of computer vision, particularly in video-to-video (V2V) synthesis, maintaining temporal consistency across video frames has been a persistent challenge. Achieving this consistency is crucial for synthesized videos’ coherence and visual appeal, which...