Optimizing AI Models with Quanto
In this article discover Quanto a powerful quantization technique designed to optimize deep learning models without compromising the performance of the model.
Prompt-based Learning Paradigm in NLP
In this blog, we discuss various types of learning paradigms present in NLP, notations often used in the prompt-based learning paradigm, demo applications of prompt-based learning, and discuss some design considerations to make while designing a prompting environment.
QwenLong-L1.5: Long-Context Reasoning
‘Learn how QwenLong-L1.5 enables long-context reasoning with advanced memory management and reinforcement learning, and how to run it on GPUs.’
RF-DETR: Real-Time Object Detection with Transformers
RF-DETR, is a state-of-the-art real-time object detection model built on transformers. Learn how it achieves high accuracy, low latency, and adaptability.
Sliding Window Attention: Efficient Long-Context Modeling
‘Learn how sliding window attention enables efficient long-context modeling in modern AI systems. Understand its benefits and use cases in LLMs.’
Text Labeling and Image Resolution with the Monkey Chat Vision Model
The Monkey Vision model, excels in generating detailed image captions and analyzing images through the Monkey Chat Vision model.
Transpose Convolution Explained for Up-Sampling Images
URL: https://www.progressiverobot.com/transpose-convolution/ Introduction Convolutional neural networks need no introduction when it comes to image processing using Deep Neural Networks (DNNs). CNNs provide a more realistic way to extract and learn features from an image, while helping reduce the image dimension. But what if we want the opposite? Suppose you want to perform some image-to-image mapping, […]
Vision Transformers (ViTs): Computer Vision with Transformer Models
Discover how Vision Transformers (ViTs) are transforming computer vision by using transformer architecture for tasks like image classification and object detection. Learn what are ViTs, inductive bias, and the working of ViTs.
Working with Different Genetic Algorithm Representations in Python
URL: https://www.progressiverobot.com/working-with-different-genetic-algorithm-representations-python/ Depending on the nature of the problem being optimized, the genetic algorithm (GA) supports two different gene representations: binary, and decimal. The binary GA has only two values for its genes, which are 0 and 1. This is easier to manage as its gene values are limited compared to the decimal GA, for […]
How to Run Z-Image-Turbo on a GPU cloud servers
‘In this tutorial we show how to run Z-Image-Turbo – the latest and greatest image generator model – on a GPU cloud servers using the ComfyUI!’
