A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

A nnie quantization aware training tool on pytorch.

Code for "Quantized Densely Connected U-Nets for Efficient Landmark Localization" (ECCV 2018) and "CU-Net: Coupled U-Nets" (BMVC 2018 oral)

LightningLM 0.1V — Reference training pipeline for the LightningLM family. 2B dense seed → 5B MoE → 9B MoE → 120B sparse MoE through TurboQuant-PreTraining on a single eight-GPU node. Companion code for *Reversible Foundations*.

An experimental platform that structures Japan's River & Sediment Control Technical Standards (Survey / Planning / Design / Maintenance editions) into a Neo4j knowledge graph and compares the performance of GPT-OSS Swallow 20B with and without GraphRAG.