Long context capability is a crucial competency for large language models (LLMs) as it
mitigates the human struggle to digest long-form texts. This capability enables complex task …

Advancements in Large Language Models (LLMs) inspire various strategies for integrating
video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface …

Large language models (LLMs) now support extremely long context windows, but the
quadratic complexity of vanilla attention results in significantly long Time-to-First-Token …

This paper provides a comprehensive overview of the principles, challenges, and
methodologies associated with quantizing large-scale neural network models. As neural …

Large language models (LLMs) are essential in natural language processing but often
struggle with inference speed and computational efficiency, limiting real-time deployment …

The widely-used, weight-only quantized large language models (LLMs), which leverage low-
bit integer (INT) weights and retain floating-point (FP) activations, reduce storage …

Large Language Models (LLMs) have revolutionized a wide range of domains such as
natural language processing, computer vision, and multi-modal tasks due to their ability to …

Large Language Models (LLMs) are widely used for tasks such as natural language and
code generation. Still, their outputs often suffer from issues like privacy violations, and …

The matrix quantization entails representing matrix elements in a more space-efficient form
to reduce storage usage, with dequantization restoring the original matrix for use. We …

Advancements in Large Language Models (LLMs) inspire various strategies for integrating
video modalities. A key approach is Video-LLMs, which incorporate an optimizable interface …