Quantization is a crucial technique in deep learning for reducing computational costs and improving model efficiency. Large-scale language models demand significant processing power, which makes quantization essential for minimizing memory usage and enhancing inference speed. By converting high-precision weights to lower-bit formats such as int8, int4, or int2, quantization reduces storage requirements. However, standard techniques…
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…