Dreaming in Cubes

We implement xFormers, a practical toolkit for fast, memory-efficient Transformer models on GPUs. We validate memory-efficient attention against a standard implementation, then compare speed and memory across sequence lengths. We work through causal masking, packed variable-length sequences, grouped-query attention, and custom ALiBi biases. Finally, we combine these into a trainable GPT-style model with SwiGLU layers and automatic mixed-precision training. The post How to Build Memory-Efficient Transformers with xFormers Using Packed Sequences, GQA, ALiBi, SwiGLU, and Causal Attention appeared first on MarkTechPost.

A retrospective on my MS thesis, the leaderboard it placed on, and the LLM shift that has reshaped the field since. The post EmoNet: Speaker-Aware Transformers for Emotion Recognition — and What I’d Build Differently in 2026 appeared first on Towards Data Science.

How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search systems step by step using Python. The post From TF-IDF to Transformers: Implementing Four Generations of Semantic Search appeared first on Towards Data Science.

How ML can change for rare events The post Using Transformers to Forecast Incredibly Rare Solar Flares appeared first on Towards Data Science.

After Donald Trump announced a pause to the US operation in the Strait of Hormuz, Iran's online propaganda machine was quick to declare victory. Explosive Media, one of the groups behind Lego-style videos mocking Trump, proclaimed it "TACO Tuesday", i.e. that the US President had “chickened out.” Meanwhile, Minecraft, the Minions, and Simpsons-style characters are joining the legions of copycats. Technology Correspondent Peter O’Brien looks at how these videos are actually made.

In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization, […] The post A End-to-End Coding Guide to Running OpenAI GPT-OSS Open-Weight Models with Advanced Inference Workflows appeared first on MarkTechPost.

From rank-stabilized scaling to quantization stability: A statistical and architectural deep dive into the optimizations powering modern Transformers. The post 6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You appeared first on Towards Data Science.