Post post-training-quantization (PTQ) method for improving LLMs. Unofficial implementation of https://arxiv.org/abs/2309.02784
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Updated
Feb 21, 2024 - Python
Post post-training-quantization (PTQ) method for improving LLMs. Unofficial implementation of https://arxiv.org/abs/2309.02784
A framework to train a ResUNet architecture, quantize, compile and execute it on an FPGA.
Research experiments archive for post-training quantization with TensorRT. Submitted and Accepted to IEEE EDGE 2024
Comprehensive study on the quantization of various CNN models, employing techniques such as Post-Training Quantization and Quantization Aware Training (QAT).
The repository discusses a research work published on MDPI Sensors and provides details about the project.
Post-training quantization on Nvidia Nemo ASR model
This sample shows how to convert TensorFlow model to OpenVINO IR model and how to quantize OpenVINO model.
Quantization for Object Detection in Tensorflow 2.x
EfficientNetV2 (Efficientnetv2-b2) and quantization int8 and fp32 (QAT and PTQ) on CK+ dataset . fine-tuning, augmentation, solving imbalanced dataset, etc.
Low-bit (2/4/8/16) Post Training Quantization for ResNet20
Improved the performance of 8-bit PTQ4DM expecially on FID.
Implementation of EPTQ - an Enhanced Post-Training Quantization algorithm for DNN compression
[ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models"
Post-Training quantization perfomed on the model trained with CLIC dataset.
Model Quantization with Pytorch, Tensorflow & Larq
Generating tensorrt model using onnx
Pytorch implementation of our paper accepted by ECCV 2022-- Fine-grained Data Distribution Alignment for Post-Training Quantization
quantization example for pqt & qat
[CVPR 2024 Highlight] TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models
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