qwix
Qwix is a Jax quantization library.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
Apache-2.0 (permissive) — 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.
last release 2026-06-22 (53 days) · last repo commit 2026-08-13 · 128 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 476,192 downloads/mo, #6,450 on PyPI
Alternatives
Verify before relying
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))- 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
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 on this page. Verify before relying on it.
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
qwix on PyPI
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.
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.
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))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 the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesjaxjaxlibflaxnumpyopt_einsumabsl-py |
| Maintenance | Actively maintained 53 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 476,192 / month, #6,450 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: qwix-0.1.8-py3-none-any.whl
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