Efficientqat: Efficient quantization-aware training for large language models
Large language models (LLMs) are crucial in modern natural language processing and
artificial intelligence. However, they face challenges in managing their significant memory …
artificial intelligence. However, they face challenges in managing their significant memory …
A survey of low-bit large language models: Basics, systems, and algorithms
Large language models (LLMs) have achieved remarkable advancements in natural
language processing, showcasing exceptional performance across various tasks. However …
language processing, showcasing exceptional performance across various tasks. However …
How good are low-bit quantized llama3 models? an empirical study
Meta's LLaMA family has become one of the most powerful open-source Large Language
Model (LLM) series. Notably, LLaMA3 models have recently been released and achieve …
Model (LLM) series. Notably, LLaMA3 models have recently been released and achieve …
An empirical study of llama3 quantization: From llms to mllms
The LLaMA family, a collection of foundation language models ranging from 7B to 65B
parameters, has become one of the most powerful open-source large language models …
parameters, has become one of the most powerful open-source large language models …
PTQ4SAM: Post-Training Quantization for Segment Anything
Abstract Segment Anything Model (SAM) has achieved impressive performance in many
computer vision tasks. However as a large-scale model the immense memory and …
computer vision tasks. However as a large-scale model the immense memory and …
A Comprehensive Approach Towards Wheat Leaf Disease Identification Leveraging Transformer Models and Federated Learning
Wheat is one of the most extensively cultivated crops worldwide that contributes significantly
to global food caloric and protein production and is grown on millions of hectares yearly …
to global food caloric and protein production and is grown on millions of hectares yearly …
Stbllm: Breaking the 1-bit barrier with structured binary llms
In this paper, we present the first structural binarization method for LLM compression to less
than 1-bit precision. Although LLMs have achieved remarkable performance, their memory …
than 1-bit precision. Although LLMs have achieved remarkable performance, their memory …
A Hybrid Framework of Transformer Encoder and Residential Conventional for Cardiovascular Disease Recognition Using Heart Sounds
Valvular heart disease (VHD) is one of the primary causes of cardiovascular illnesses with
high mortality rates worldwide. Early detection of VHD enables optimal treatment and stops …
high mortality rates worldwide. Early detection of VHD enables optimal treatment and stops …
Knowledge Augmentation for Distillation: A General and Effective Approach to Enhance Knowledge Distillation
Y Tang, Z Guo, L Wang, B Fan, F Cao, K Gao… - Proceedings of the 1st …, 2024 - dl.acm.org
Knowledge Distillation (KD), which extracts knowledge from a well-performed large neural
network (aka teacher network) to guide the training of a small network (aka student network) …
network (aka teacher network) to guide the training of a small network (aka student network) …
QVD: Post-training Quantization for Video Diffusion Models
Recently, video diffusion models (VDMs) have garnered significant attention due to their
notable advancements in generating coherent and realistic video content. However …
notable advancements in generating coherent and realistic video content. However …