repoGitHubTrust 82 · PrimaryPublished 2mo agoLive · 3mo ago
quant-kit
Post-training quantization tools for transformers.
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via unknownBuild your first transformer from scratch →
- LinkedLinked via unknownQuantBench →
- LinkedLinked via unknownQuantization at 1.58 bits →
- LinkedLinked via unknownW4A4 Quantization for Inference on Wan2.2-I2V-A14B →
- LinkedLinked via unknownGeneralization Analysis of Transformers in Distribution Regression →
- LinkedLinked via unknownZero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models →
- LinkedLinked via unknownPost-Training Pruning for Diffusion Transformers →
- LinkedLinked via unknown$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space →
Related to
Related to (incoming)
Implements (incoming)
paperQuantization at 1.58 bitspaperW4A4 Quantization for Inference on Wan2.2-I2V-A14BpaperGeneralization Analysis of Transformers in Distribution RegressionpaperZero-Shot Quantization for Object Detectors using Off-the-Shelf Generative ModelspaperPost-Training Pruning for Diffusion Transformerspaper$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic SpacepaperThe State-Prediction Separation HypothesispaperOrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
Covers (incoming)
Related across the graph
paperThe State-Prediction Separation HypothesispaperZero-Shot Quantization for Object Detectors using Off-the-Shelf Generative ModelspaperQuantization at 1.58 bitstutorialBuild your first transformer from scratchpaperOrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion TransformerspaperW4A4 Quantization for Inference on Wan2.2-I2V-A14BnewsH64LM: A 249M-parameter Mixture-of-Experts Transformer built from scratch in PyTorch [P]toolQuantBenchpaperGeneralization Analysis of Transformers in Distribution RegressionnewsTrain and run transformers directly on Apple's Neural Enginepaper$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic SpacepaperPost-Training Pruning for Diffusion Transformers