In today’s digital landscape, automating interactions with web content remains a nuanced challenge. Many existing solutions are resource-intensive and tailored for narrowly defined tasks, which limits their broader applicability. Developers often face the dual challenge of balancing computational efficiency with the need for a model that can generalize well across diverse websites. Traditional systems, heavily…
In this tutorial, we will guide you through building an advanced financial data reporting tool on Google Colab by combining multiple Python libraries. You’ll learn how to scrape live financial data from web pages, retrieve historical stock data using yfinance, and visualize trends with matplotlib. Also, the tutorial demonstrates how to integrate an interactive UI…
Pre-trained LLMs require instruction tuning to align with human preferences. Still, the vast data collection and rapid model iteration often lead to oversaturation, making efficient data selection a crucial yet underexplored area. Existing quality-driven selection methods, such as LIMA and AlpaGasus, tend to overlook the importance of data diversity and complexity, essential for enhancing model…
Optimizing large-scale language models demands advanced training techniques that reduce computational costs while maintaining high performance. Optimization algorithms are crucial in determining training efficiency, particularly in large models with extensive parameter counts. While optimizers like AdamW have been widely adopted, they often require meticulous hyperparameter tuning and high computational resources. Finding a more efficient alternative…
Large-scale reinforcement learning (RL) training of language models on reasoning tasks has become a promising technique for mastering complex problem-solving skills. Currently, methods like OpenAI’s o1 and DeepSeek’s R1-Zero, have demonstrated remarkable training time scaling phenomenon. Both models’ benchmark performance and response length consistently and steadily increase without any sign of saturation as the training…
Large language models that use the Mixture-of-Experts (MoE) architecture have enabled significant increases in model capacity without a corresponding rise in computation. However, this approach also introduces challenges—especially when it comes to communication between GPUs. In MoE models, only a subset of experts is active for any given token, so efficiently exchanging data among devices…
In this tutorial, we will build an interactive web scraping project in Google Colab! This guide will walk you through extracting live weather forecast data from the U.S. National Weather Service. You’ll learn to set up your environment, write a Python script using BeautifulSoup and requests, and integrate an interactive UI with ipywidgets. This tutorial…
Artificial intelligence continues to advance in natural language processing but still faces challenges in spatial reasoning tasks. Visual-spatial reasoning is fundamental for robotics, autonomous navigation, and interactive problem-solving applications. AI systems must effectively interpret structured environments and execute sequential decisions to function in these domains. While traditional maze-solving algorithms, such as depth-first search and A*,…
Recent advancements in LLMs have significantly improved their reasoning abilities, enabling them to perform text composition, code generation, and logical deduction tasks. However, these models often struggle with balancing their internal knowledge and external tool use, leading to Tool Overuse. This occurs when LLMs unnecessarily rely on external tools for tasks that their parametric knowledge…
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…