This is the official PyTorch implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models"
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Updated
Jul 17, 2024 - Python
This is the official PyTorch implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models"
Brevitas: neural network quantization in PyTorch
Quantization of Models : Post-Training Quantization(PTQ) and Quantize Aware Training(QAT)
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.
[ICML 2024] Outlier-Efficient Hopfield Layers for Large Transformer-Based Models
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
EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.
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
Build AI model to classify beverages for blind individuals
quantization example for pqt & qat
Generating tensorrt model using onnx
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