The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a
groundbreaking paradigm for intelligent edge devices. With their capacity for human-like …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

Generative Large Language Models (LLMs) have demonstrated remarkable results for a
wide range of tasks. However, deploying these models for inference has been a significant …

We introduce QuaRot, a new Quantization scheme based on Rotations, which is able to
quantize LLMs end-to-end, including all weights, activations, and KV cache in 4 bits. QuaRot …

Large language models (LLMs) are crucial in modern natural language processing and
artificial intelligence. However, they face challenges in managing their significant memory …

Large language models (LLMs) have achieved remarkable advancements in natural
language processing, showcasing exceptional performance across various tasks. However …

As inference on Large Language Models (LLMs) emerges as an important workload in
machine learning applications, weight quantization has become a standard technique for …

The deployment of large language models (LLMs) is often constrained by memory
bandwidth, where the primary bottleneck is the cost of transferring model parameters from …

Scaling model size significantly challenges the deployment and inference of Large
Language Models (LLMs). Due to the redundancy in LLM weights, recent research has …

Recent advancements in large language models (LLMs) are propelling us toward artificial
general intelligence with their remarkable emergent abilities and reasoning capabilities …