Introduction GitHub is an essential platform for version control and collaboration. This guide will walk you through three fundamental GitHub skills: creating and uploading a repository, cloning an existing repository, and writing an effective README file. By following these step-by-step instructions, you’ll be able to start using GitHub efficiently for your projects. 1. Creating and…
The ambition to accelerate scientific discovery through AI has been longstanding, with early efforts such as the Oak Ridge Applied AI Project dating back to 1979. More recent advancements in foundation models have demonstrated the feasibility of fully automated research pipelines, enabling AI systems to autonomously conduct literature reviews, formulate hypotheses, design experiments, analyze results,…
In today’s data-driven world, having access to powerful computing resources is essential for developers, data scientists, and students alike. Enter Google Colab – a game-changing platform that provides free access to cloud computing resources, including GPU support, without requiring any local setup. Whether you’re a beginner taking your first steps in Python programming or an…
Proteins are the essential component behind nearly all biological processes, from catalyzing reactions to transmitting signals within cells. While advances like AlphaFold have transformed our ability to predict static protein structures, a fundamental challenge remains: understanding the dynamic behavior of proteins. Proteins naturally exist as ensembles of interchanging conformations that underpin their function. Traditional experimental…
In this tutorial, we will build an efficient Legal AI CHatbot using open-source tools. It provides a step-by-step guide to creating a chatbot using bigscience/T0pp LLM, Hugging Face Transformers, and PyTorch. We will walk you through setting up the model, optimizing performance using PyTorch, and ensuring an efficient and accessible AI-powered legal assistant. Copy CodeCopiedUse…
Large Language Models (LLMs) face significant challenges in optimizing their post-training methods, particularly in balancing Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) approaches. While SFT uses direct instruction-response pairs and RL methods like RLHF use preference-based learning, the optimal allocation of limited training resources between these approaches remains unclear. Recent studies have shown that models…
The development of high-performing machine learning models remains a time-consuming and resource-intensive process. Engineers and researchers spend significant time fine-tuning models, optimizing hyperparameters, and iterating through various architectures to achieve the best results. This manual process demands computational power and relies heavily on domain expertise. Efforts to automate these aspects have led to the development…