---
id: qwix
version: "0.1.8"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# qwix — Qwix is a Jax quantization library.

License: permissive · Maintenance: active · Downloads: 476.2K/mo

## What it is and what it does

Qwix is a JAX quantization library that reduces model size and accelerates inference by converting neural network weights and activations to lower-precision numeric types (int4, int8, fp8, and emulated formats). It integrates with Flax models without requiring code changes and supports three main workflows: QAT (fake quantization during training), PTQ (post-training quantization for XLA devices), and ODML (annotation for LiteRT conversion). The library uses a regex-based configuration system to define quantization rules per module, allowing fine-grained control over which layers are quantized and how.

Qwix is designed for practitioners who want to deploy JAX models efficiently on resource-constrained devices or accelerators. It handles the complexity of calibration (absmax, minmax, rms, fixed), granularity (per-channel and sub-channel), and operator-specific quantization strategies for both XLA targets (CPU/GPU/TPU) and mobile/edge targets via LiteRT. The package is actively maintained, in alpha status, and carries no known security vulnerabilities.

Use it for:

- Reduce model size for deployment on mobile or edge devices using LiteRT quantization with full integer arithmetic.
- Speed up inference on TPU/GPU by applying post-training quantization to existing trained models without retraining.
- Train models with quantization awareness using fake quantization to simulate low-precision behavior during training.
- Apply LoRA/QLoRA fine-tuning to quantized models for efficient adaptation to downstream tasks.
- Experiment with different quantization schemas (weight-only, dynamic-range, static-range) via configuration without modifying model code.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Qwix is a JAX quantization library that applies Quantization-Aware Training (QAT) and Post-Training Quantization (PTQ) to neural network models, supporting deployment on XLA devices (CPU/GPU/TPU) and LiteRT targets.

Yes, if you are working with JAX models and need production-ready quantization. The package is actively maintained, has no known vulnerabilities, uses a permissive license, and integrates seamlessly with Flax. Install friction is low for users already in the JAX ecosystem. The alpha status and small community (128 GitHub stars) mean fewer battle-tested recipes, so verify quantization quality for your specific models before production use.

## Install

pip install qwix
uv add qwix
poetry add qwix

## Installing qwix

Before you install: Low install friction with a pure-Python wheel. The package is actively maintained (last commit 2026-08-13) and in alpha status. It depends on six heavy scientific libraries (jax, jaxlib, flax, numpy, opt_einsum, absl-py), all of which are standard in the JAX ecosystem, so installation complexity is typical for JAX projects rather than exceptional.

License in practice: Licensed under Apache-2.0 (permissive), so you can use, modify, and distribute the package freely in both open-source and commercial projects without copyleft obligations.

Quickstart:

pip install qwix

import qwix
import jax
from flax import linen as nn

rules = [qwix.QuantizationRule(module_path='.*', weight_qtype='int8', act_qtype='int8')]
ptq_model = qwix.quantize_model(model, qwix.PtqProvider(rules))

Requires JAX and jaxlib to be installed; jaxlib installation can be complex depending on your hardware (CPU-only, GPU, or TPU). Requires Python 3.10 or later.

Verify before relying:

- Whether the package is available on PyPI or still requires installation from GitHub (description says 'doesn't provide a PyPI package yet' but version 0.1.8 appears in PyPI)
- Performance impact of QAT fake quantization versus true quantized training on model convergence
- Compatibility with Flax NNX models beyond Flax Linen

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 476.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

jax quantization library, post-training quantization, quantization-aware training, model compression jax, int8 quantization jax, neural network quantization, weight quantization, model-compression, jax-ecosystem, quantization

[View on SkillFed](https://skillfed.io/packages/qwix) · [View on PyPI](https://pypi.org/project/qwix/)