Beyond Market Intelligence/GPT-2

GPT-2

GPT-2 on Beyond Market Intelligence: a running collection of 5 stories we have gathered and hand-picked because they are worth your time. Every post here touches on gpt-2 in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around gpt-2, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

I released a softmax-free attention model at GPT-2 Medium scale (~354M params, 11.5B tokens): structural sparsity + tile-skipping kernels for long-context VRAM savings. Open weights + custom Triton kernels [R]
Machine Learning

I released a softmax-free attention model at GPT-2 Medium scale (~354M params, 11.5B tokens): structural sparsity + tile-skipping kernels for long-context VRAM savings. Open weights + custom Triton kernels [R]

A significant advancement in long-context AI modeling has emerged: a softmax-free attention model, released at GPT-2 Medium scale (~354M parameters, 11.5B tokens). This innovative approach leverages structural sparsity and tile-skipping kernels to dramatically reduce VRAM requirements, making it accessible for broader application. Open weights and custom Triton kernels are available. This work builds upon ongoing explorations of optimization techniques, such as those discussed in "Python packages for particle swarms, genetic algorithms," demonstrating a continued focus on efficient AI solutions. [link]

Machine Learning

I built a tool that shows you what GPT-2 is "thinking" in real-time as it generates 3D graph of concept activations per token [R]

Introducing AXON, a groundbreaking tool that visualizes what GPT-2 "thinks" in real-time as it generates language. By leveraging a Sparse Autoencoder to decode the model's residual stream, AXON creates a dynamic 3D graph of concept activations per token. This innovative approach reveals fascinating insights, such as how geography and language features light up moments before specific tokens are generated, enhancing our understanding of model behavior.

Machine Learning

AI/ML Ethicists [D]

As AI and machine learning continue to evolve, many professionals grapple with the ethical implications of this technology. A user recently shared their journey, expressing both excitement for AI's potential and concern about its impact on society, particularly regarding jobs and mental health. They seek insights from AI ethicists about the viability of ethics as a career path and the tangible differences one can make in this field.

Transformer Math Explorer [P]
Machine Learning

Transformer Math Explorer [P]

Introducing the Transformer Math Explorer, an interactive math reference designed to demystify transformer models through intuitive dataflow graphs. Covering a range from GPT-2 to Qwen 3.6, this tool allows users to toggle between various models and concepts, including MLA, MoE, RoPE, MTP, and hybrid attention. Originally created for personal use, it aims to provide clarity in understanding complex variations. If you encounter any errors or find aspects that are unclear, your feedback is invaluable for enhancing this resource.

Machine Learning

[R] Depth-first pruning transfers: GPT-2 → TinyLlama with stable gains and minimal loss

Depth-first pruning offers a promising approach to optimizing transformer models like GPT-2 and TinyLlama. By strategically removing entire layers based on their sensitivity, this method achieves an 8–12% reduction in model size with only a 6–8% quality loss. Experimentation with TinyLlama 1.1B confirms the technique's stability and efficiency, yielding real inference speedups without compromising overall performance. This insight highlights that not all layers hold equal importance, emphasizing the value of targeted pruning over uniform scaling.