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 …
Compressing large language models by joint sparsification and quantization
In this paper, we introduce a novel model compression technique named Joint Sparsification
and Quantization (JSQ), explicitly tailored for large language models (LLMs). Traditional …
and Quantization (JSQ), explicitly tailored for large language models (LLMs). Traditional …
[HTML][HTML] Enhancing brain tumor segmentation in MRI images: A hybrid approach using UNet, attention mechanisms, and transformers
Accurate brain tumor segmentation in MRI images is crucial for effective treatment planning
and monitoring. Traditional methods often encounter challenges due to the complexity and …
and monitoring. Traditional methods often encounter challenges due to the complexity and …
[HTML][HTML] Enhancing medical image classification via federated learning and pre-trained model
The precise classification of medical images is crucial in various healthcare applications,
especially in fields like disease diagnosis and treatment planning. In recent times, machine …
especially in fields like disease diagnosis and treatment planning. In recent times, machine …
Q-snns: Quantized spiking neural networks
Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to represent
information and process them in an asynchronous event-driven manner, offering an energy …
information and process them in an asynchronous event-driven manner, offering an energy …
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 …
On-Device LLMs for SMEs: Challenges and Opportunities
This paper presents a systematic review of the infrastructure requirements for deploying
Large Language Models (LLMs) on-device within the context of small and medium-sized …
Large Language Models (LLMs) on-device within the context of small and medium-sized …