PyTorch 101 Memory Management and Using Multiple GPUs
Explore PyTorch’s advanced GPU management, multi-GPU usage with data and model parallelism, and best practices for debugging memory errors.
Getting Started with LLMs for Social Media Analytics with 1-Click Models
This article explains how LLMs can be used for analyzing social media data with prompt engineering and includes a tutorial on setting up a gradio interface for 1-Click Models powered by HuggingFace and run on the cloud provider’s GPU Droplets
Decoding Long-CLIP: Understand the Power of Zero-Shot Classification
In this article, we present Long-CLIP, a fine-tuning method for CLIP that maintains original capabilities through two new strategies: (1) preserving knowledge via positional embedding stretching and (2) matching CLIP features’ primary components efficiently.
Understanding Parallel Computing: GPUs vs CPUs Explained Simply with role of CUDA
In this article we will understand the role of CUDA, and how GPU and CPU play distinct roles, to enhance performance and efficiency.
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.
Mastering Apache Iceberg for Scalable Data Lake Management
Learn how to use Apache Iceberg to build fast, scalable, and reliable data lakes. We will walk through the essentials of managing big data with confidence.
Designing Hardware-Aware Algorithms: FlashAttention
‘This article explains the techniques that made FlashAttention (2022) successful in achieving wall-clock speedup over the standard attention mechanism.’
LangMem SDK for Agent Long-Term Memory
Explore the LangMem SDK for agent long-term memory features, architecture, and how it enables persistent, context-aware AI agents.
Introduction to DETR – Part 2: The Crucial Role of the Hungarian Algorithm
In part 2 of this tutorial series, we look at DETR’s Hungarian Algorithm in depth to show how it minimizes cost.
Finetune Multimodel LLM:IDEFICS 9B using A100
In this article, we will learn how to make predictions using the 4-bit quantized Idefics-9B model and fine-tune it on a specific dataset.
