Personalizing Language Models for Business Applications Personalizing large language models (LLMs) is crucial for enhancing applications like virtual assistants and content recommendations. This ensures that responses are tailored to individual user preferences. Challenges with Traditional Approaches Traditional methods optimize models based on aggregated user feedback, which can overlook the unique perspectives shaped by culture and…

Introduction to Hugging Face’s SmolAgents Framework Hugging Face’s SmolAgents framework offers a simple and efficient method for creating AI agents that utilize tools such as web search and code execution. This guide illustrates how to develop an AI-powered research assistant capable of autonomously searching the web and summarizing articles using SmolAgents. The implementation is straightforward,…

Introduction Scientific publishing has grown significantly in recent decades. However, access to vital research remains limited for many, especially in developing countries, independent researchers, and small academic institutions. Rising journal subscription costs worsen this issue, restricting knowledge availability even in well-funded universities. Despite the push for Open Access (OA), barriers persist, as seen in access…

In-Context Learning (ICL) in Large Language Models In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks with minimal examples. This capability enhances model flexibility and efficiency, making it valuable for applications like language translation, text summarization, and automated reasoning. However, the mechanisms behind ICL are still being researched, with two main…

Understanding AI Agents and Agentic AI Artificial intelligence has advanced significantly, evolving from simple systems to sophisticated entities capable of performing complex tasks. This article discusses two key concepts: AI Agents and Agentic AI. While they may seem similar, they represent different approaches to intelligent systems. Definitions and Key Concepts AI Agents An AI agent…

Challenges with Large Language Models Large language models have greatly improved our understanding of artificial intelligence, but efficiently scaling these models still poses challenges. Traditional Mixture-of-Experts (MoE) architectures activate only a few experts for each token to save on computation. This design, however, leads to two main issues: Experts work independently, limiting the model’s ability…

Challenges in Internal Data Research Modern businesses encounter numerous obstacles in internal data research. Data is often dispersed across various sources such as spreadsheets, databases, PDFs, and online platforms, complicating the extraction of coherent insights. Organizations frequently face disjointed systems where structured SQL queries and unstructured documents do not integrate smoothly. This fragmentation impedes decision-making…

Enhancing Large Language Models for Efficient Reasoning Improving the ability of large language models (LLMs) to perform complex reasoning tasks while minimizing computational costs is a significant challenge. Generating multiple reasoning steps and selecting the best answer can enhance accuracy but requires substantial memory and computing power. Long reasoning chains or large batches can be…

Challenges in Modern Data Workflows Organizations are facing difficulties with increasing dataset sizes and complex distributed processing. Traditional systems often struggle with slow processing times, memory limitations, and effective management of distributed tasks. Consequently, data scientists and engineers spend more time on system maintenance instead of deriving insights from data. There is a clear need…

Introduction to Large Language Models in Medicine Large Language Models (LLMs) are increasingly utilized in the medical field for tasks such as diagnostics, patient sorting, clinical reporting, and research workflows. While they perform well in controlled settings, their effectiveness in real-world applications remains largely untested. Challenges with Current Evaluations Most evaluations of LLMs rely on…