As LLM-powered agents move from research to production, one design tension is becoming harder to ignore: the more useful cloud-hosted […]
Category: Machine Learning
Stochastic Gradient Descent (SGD’s) Frequency Bias and How Adam Fixes It
Modern language models are trained on data with extremely uneven token distributions. A small number of words appear in almost […]
NVIDIA Introduces a 4-Bit Pretraining Methodology Using NVFP4, Validated on a 12B Hybrid Mamba-Transformer at 10T Token Horizon
Pretraining frontier-scale LLMs in FP8 is now standard practice, but moving to 4-bit floating point has remained an open research […]
A Coding Implementation to Compress and Benchmark Instruction-Tuned LLMs with FP8, GPTQ, and SmoothQuant Quantization using llmcompressor
In this tutorial, we explore how to apply post-training quantization to an instruction-tuned language model using llmcompressor. We start with […]
A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models
In this tutorial, we implement SHAP workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots. […]
Nous Research Proposes Lighthouse Attention: A Training-Only Selection-Based Hierarchical Attention That Delivers 1.4–1.7× Pretraining Speedup at Long Context
Training large language models on long sequences has a well-known problem: attention is expensive. The scaled dot-product attention (SDPA) at […]
Meet LiteLLM Agent Platform: A Kubernetes-Based, Self-Hosted Infrastructure Layer for Isolated Agent Sandboxes and Persistent Session Management in Production
Running AI agents in a local script is straightforward. Running them reliably in production across teams, across restarts, with isolated […]
NVIDIA Introduces SANA-WM: A 2.6B-Parameter Open-Source World Model That Generates Minute-Scale 720p Video on a Single GPU
World models (systems that synthesize realistic video sequences from an initial image and a set of actions) are becoming central […]
Zyphra Releases ZAYA1-8B-Diffusion-Preview: The First MoE Diffusion Model Converted From an Autoregressive LLM With Up to 7.7x Speedup
Zyphra, the San Francisco-based AI lab behind the ZAYA1 model family, released ZAYA1-8B-Diffusion-Preview — a preview of its early work […]
Nous Research Releases Token Superposition Training to Speed Up LLM Pre-Training by Up to 2.5x Across 270M to 10B Parameter Models
Pre-training large language models is expensive enough that even modest efficiency improvements can translate into meaningful cost and time savings. […]