Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Generative Pre-trained Transformer models, known as GPT or OPT, set themselves apart
through breakthrough performance across complex language modelling tasks, but also by …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Generative Pre-trained Transformer models, known as GPT or OPT, set themselves apart
through breakthrough performance across complex language modelling tasks, but also by …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

The recent emergence of Large Language Models based on the Transformer architecture
has enabled dramatic advancements in the field of Natural Language Processing. However …

Post-training quantization (PTQ) has emerged as a promising technique for mitigating
memory consumption and computational costs in large language models (LLMs). However …

Improving the deployment efficiency of transformer-based language models has been
challenging given their high computation and memory cost. While INT8 quantization has …

Post-training quantization (PTQ) has emerged as a promising technique for mitigating
memory consumption and computational costs in large language models (LLMs). However …

In the complex domain of large language models (LLMs), striking a balance between
computational efficiency and maintaining model quality is a formidable challenge …