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…