Large Language Models (LLMs) have gained significant importance as productivity tools, with open-source models increasingly matching the performance of their closed-source counterparts. These models operate through Next Token Prediction, where tokens are predicted in sequence when computing attention is between each token and its predecessors. Key-value (KV) pairs are cached to prevent redundant calculations and…
Most modern visualization authoring tools like Charticulator, Data Illustrator, and Lyra, and libraries like ggplot2, and VegaLite expect tidy data, where every variable to be visualized is a column and each observation is a row. When the input data is in a tidy format, authors simply need to bind data columns to visual channels, otherwise,…
Large language models (LLMs) process extensive datasets to generate coherent outputs, focusing on refining chain-of-thought (CoT) reasoning. This methodology enables models to break down intricate problems into sequential steps, closely emulating human-like logical reasoning. Generating structured reasoning responses has been a major challenge, often requiring extensive computational resources and large-scale datasets to achieve optimal performance.…
In recent years, the rapid scaling of large language models (LLMs) has led to extraordinary improvements in natural language understanding and reasoning capabilities. However, this progress comes with a significant caveat: the inference process—generating responses one token at a time—remains a computational bottleneck. As LLMs grow in size and complexity, the latency and energy demands…
LLMs have demonstrated exceptional capabilities, but their substantial computational demands pose significant challenges for large-scale deployment. While previous studies indicate that intermediate layers in deep neural networks can be reordered or removed without severely impacting performance, these insights have not been systematically leveraged to reduce inference costs. Given the rapid expansion of LLMs, which often…
Large Language Models (LLMs) have revolutionized natural language processing (NLP) but face significant challenges in practical applications due to their large computational demands. While scaling these models improves performance, it creates substantial resource constraints in real-time applications. Current solutions like MoE Mixture of Experts (MoE) enhance training efficiency through selective parameter activation but suffer slower…
Introduction In this tutorial, we will build an advanced AI-powered news agent that can search the web for the latest news on a given topic and summarize the results. This agent follows a structured workflow: Browsing: Generate relevant search queries and collect information from the web. Writing: Extracts and compiles news summaries from the collected…
The Open O1 project is a groundbreaking initiative aimed at matching the powerful capabilities of proprietary models, particularly OpenAI’s O1, through an open-source approach. By leveraging advanced training methodologies and community-driven development, Open O1 seeks to democratize access to state-of-the-art AI models. Proprietary AI models like OpenAI’s O1 have demonstrated exceptional capabilities in reasoning, tool…
Large language model (LLM)–based AI companions have evolved from simple chatbots into entities that users perceive as friends, partners, or even family members. Yet, despite their human-like capability, the AI companions often make biased, discriminatory, and harmful claims. These biases are capable of enforcing inherent stereotypes and causing psychological suffering, particularly in marginalized communities. Conventional…
Machines learn to connect images and text by training on large datasets, where more data helps models recognize patterns and improve accuracy. Vision-language models (VLMs) rely on these datasets to perform tasks like image captioning and visual question answering. The question is whether increasing datasets to 100 billion examples dramatically improves accuracy, cultural diversity, and…