Language models (LMs) have great capabilities as in-context learners when pretrained on vast internet text corpora, allowing them to generalize effectively from just a few task examples. However, fine-tuning these models for downstream tasks presents significant challenges. While fine-tuning requires…

Fine-tuning LLMs often requires extensive resources, time, and memory, challenges that can hinder rapid experimentation and deployment. Unsloth AI revolutionizes this process by enabling fast, efficient fine-tuning state-of-the-art models like Qwen3-14B with minimal GPU memory, leveraging advanced techniques such as…

Meta has introduced KernelLLM, an 8-billion-parameter language model fine-tuned from Llama 3.1 Instruct, aimed at automating the translation of PyTorch modules into efficient Triton GPU kernels. This initiative seeks to lower the barriers to GPU programming by simplifying kernel development…

Google has officially rolled out the NotebookLM mobile app, extending its AI-powered research assistant to Android devices. The app aims to bring personalized learning and content synthesis directly to users’ pockets by introducing new features that combine mobility, context-awareness, and…

While RAG enables responses without extensive model retraining, current evaluation frameworks focus on accuracy and relevance for answerable questions, neglecting the crucial ability to reject unsuitable or unanswerable requests. This creates high risks in real-world applications where inappropriate responses can…

The ability to search high-dimensional vector representations has become a core requirement for modern data systems. These vector representations, generated by deep learning models, encapsulate data’s semantic and contextual meanings. This enables systems to retrieve results not based on exact…