lastmile-ai/mcp-agentjust droppedNEW
dottxt-ai/outlinesjust droppedNEW
getomni-ai/zeroxjust droppedPIPETrendingRepoLIVE · 30 mention sourcesNEWlastmile-ai/mcp-agentjust droppedNEWdottxt-ai/outlinesjust droppedNEWgetomni-ai/zeroxjust droppedPIPETrendingRepoLIVE · 30 mention sourcesA transformer is the neural-network architecture behind modern language models, using self-attention to weigh how every token in the input relates to every other.
The transformer replaced recurrent networks by processing a whole sequence in parallel and letting each token attend to all others through self-attention. That design scales efficiently on modern hardware and underlies essentially every current large language model.
Open-source implementations of the architecture — and the model weights built on it — let teams study, run, and adapt transformers directly rather than only through a hosted API.
SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformer
Learn it. Build it. Ship it for others.
A hand-curated library of the best machine learning education — 590 docs (78 arXiv papers, 474 course lectures from Stanford/MIT/Karpathy/fast.ai, 38 explainer articles), normalized to Markdown with full provenance. A clean ML corpus/dataset for learning, RAG, and fine-tuning.
Official PyTorch implementation of "Steerable Rhythmic Complexity in Autoregressive Music Generation" (EI Accepted). A bar-level conditional micro-Transformer using REMI+ syntax for exact density control and harmonic decoupling.
3D explorable atlas of every neuron in a transformer LLM. Click neurons, trace residual highways, compare cross-architecture conservation. Live demo: https://charenix.com/qwen3b-atlas
Build a modern LLM from scratch. Every line commented. Explained like we are five.
A theoretical reconstruction of the Claude Mythos architecture, built from first principles using the available research literature.