Conversion and Quantization of Multimodal Large Models for Robots

Conversion and Quantization of Multimodal Large Models for Robots

1. Introduction In today’s field of artificial intelligence, the application of multimodal large models in robotics is becoming increasingly widespread. This article aims to introduce how to convert multimodal large models to the gguf format and quantize them for efficient deployment on the ollama platform. Through this process, we achieve more efficient model operation and … Read more

Overview of Compact 1-Bit Convolutional Neural Networks via Bayesian Learning

Overview of Compact 1-Bit Convolutional Neural Networks via Bayesian Learning

The “Quick Overview” series of articles aims to disseminate important results from conferences and journals in the field of image graphics, allowing readers to quickly understand relevant academic dynamics in their native language through short articles. We welcome attention and submissions~ ◆ ◆ ◆ ◆ Compact 1-Bit Convolutional Neural Networks (BONN) Based on Bayesian Learning … Read more

6 Methods for Compressing Convolutional Neural Networks

6 Methods for Compressing Convolutional Neural Networks

This articleis approximately 5200 words, recommended reading time is10+minutes We know that, to some extent, the deeper the network, the more parameters it has, and the more complex the model, the better its final performance. The compression algorithm for neural networks aims to transform a large and complex pre-trained model into a streamlined smaller model. … Read more