Brevitas: neural network quantization in PyTorch
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Updated
Jun 12, 2024 - Python
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 PyTorch implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models", and also an efficient LLM compression tool with various advanced compression methods, supporting multiple inference backends.
[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.
Quantization of Models : Post-Training Quantization(PTQ) and Quantize Aware Training(QAT)
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