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.

mi-optimize is a versatile tool designed for the quantization and evaluation of large language models (LLMs). The library's seamless integration of various quantization methods and evaluation techniques empowers users to customize their approaches according to specific requirements and constraints, providing a high level of flexibility.

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

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

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

inference with the structured sparsity and quantization

deep insight tensorrt, qat, ptq, plugin, triton_inference, cuda

LLM quantization techniques: absmax, zero-point, GPTQ and GGUF

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