Brevitas: neural network quantization in PyTorch

More readable and flexible yolov5 with more backbone(gcn, resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer, etc) and (cbam，dcn and so on), and tensorrt

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

This is the official implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models", and it is also an efficient LLM compression tool with various advanced compression methods, supporting multiple inference backends.

Deep Learning Model Optimization Using by TensorRT API, window

[ICML 2024] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models

quantization example for pqt & qat

Generating tensorrt model using onnx

Build AI model to classify beverages for blind individuals

Post post-training-quantization (PTQ) method for improving LLMs. Unofficial implementation of https://arxiv.org/abs/2309.02784

inference with the structured sparsity and quantization

EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.

deep insight tensorrt

Quantization of Models : Post-Training Quantization(PTQ) and Quantize Aware Training(QAT)