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Llama-3.2–1 B-Instruct and LanceDBAbstract: Retrieval-augmented generation (RAG) combines large language models with external knowledge sources to produce more accurate and contextually relevant responses. This article explores how…

Imagine turning your often-overlooked accounts payable (AP) department into a strategic powerhouse. While businesses focus on optimizing every corner of their…

Imagine turning your often-overlooked Accounts Payable (AP) department into a strategic powerhouse. While businesses focus on optimizing every corner of their…

Large language models (LLMs) have revolutionized how machines process and generate human language, but their ability to reason effectively across diverse tasks remains a significant challenge.…

IntroductionLarge Language Models (LLMs), no matter how advanced or powerful, fundamentally operate as next-token predictors. One well-known limitation of these models…

Multimodal AI models are powerful tools capable of both understanding and generating visual content. However, existing approaches often use a single visual encoder for both tasks,…

Vision-Language Models (VLMs) struggle with spatial reasoning tasks like object localization, counting, and relational question-answering. This issue stems from Vision Transformers (ViTs) trained with image-level supervision,…

Looking back at AI progress since the 2012 blog post “The state of Computer Vision and AI: we are really, really far away”President Barack Obama jokingly…

Create a shareable HTML document with your code, outputs, and graphsWhen collaborating on data projects, sharing your work effectively is crucial. While sending over commented code…

One of the biggest hurdles organizations face is implementing Large Language Models (LLMs) to handle intricate workflows effectively. Issues of speed, flexibility, and scalability often hinder…