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Toolkit for efficient experimentation with Speech Recognition, Text2Speech and NLP

Microsoft Automatic Mixed Precision Library

Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

[CVPR 2019, Oral] HAQ: Hardware-Aware Automated Quantization with Mixed Precision

Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation

[ICLR'25] ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation

FP64 equivalent GEMM by the Ozaki scheme with Int8 Tensor Cores

High Resolution Style Transfer in PyTorch with Color Control and Mixed Precision 🎨

A tool for debugging and assessing floating point precision and reproducibility.

Get more intelligence from every bit. Better quantization formats and smarter calibration let larger, stronger models run smoothly on the hardware you already own.

<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소

Training with FP16 weights in PyTorch

NAACL '24 (Best Demo Paper RunnerUp) / MlSys @ NeurIPS '23 - RedCoast: A Lightweight Tool to Automate Distributed Training and Inference

基于tensorflow1.x的预训练模型调用，支持单机多卡、梯度累积，XLA加速，混合精度。可灵活训练、验证、预测。

BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

TensorFlow 1.x BERT Training with Multi-GPU Gradient Accumulation XLA Mixed Precision 2026

🎯 Gradient Accumulation for TensorFlow 2

CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices

[ECCV24] MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization