semvec/pypi-packages huggingface dataset:
aqtptest 0.0.4
AQT: Accurate Quantized Training

# Accurate Quantized Training

This directory contains libraries for running and analyzing
neural network quantization experiments in JAX and flax.

Summary about this work is presented at paper [Pareto-Optimal Quantized ResNet Is Mostly 4-bit](https://arxiv.org/abs/2105.03536).
Please cite the paper in your publications if you find the source code useful for your research.

Contributors: Shivani Agrawal, Lisa Wang, Jonathan Malmaud, Lukasz Lew,
Pouya Dormiani, Phoenix Meadowlark, Oleg Rybakov.

## AQT Quantization Library

`Jax` and `Flax` quantization libraries provides `what you serve is what you train`
quantization for convolution and matmul. See [this README.md](./jax/README.md).

## Reporting Tool

After a training run has completed, the reporting tool in
`report_utils.py` allows to generate a concise experiment report with aggregated
metrics and metadata. See [this README.md](./utils/README.md).