Language models have become increasingly expensive to train and deploy. This has led researchers to explore techniques such as model distillation, where a smaller student model is trained to replicate the performance of a larger teacher model. The idea is to enable efficient deployment without compromising performance. Understanding the principles behind distillation and how computational… →

Large Language Models (LLMs) have advanced significantly in natural language processing, yet reasoning remains a persistent challenge. While tasks such as mathematical problem-solving and code generation benefit from structured training data, broader reasoning tasks—like logical deduction, scientific inference, and symbolic reasoning—suffer from sparse and fragmented data. Traditional approaches, such as continual pretraining on code, often… →

Large language models (LLMs) have demonstrated exceptional problem-solving abilities, yet complex reasoning tasks—such as competition-level mathematics or intricate code generation—remain challenging. These tasks demand precise navigation through vast solution spaces and meticulous step-by-step deliberation. Existing methods, while improving accuracy, often suffer from high computational costs, rigid search strategies, and difficulty generalizing across diverse problems. In… →

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.… →