# AI-Efficiency/Awesome-Model-Quantization

A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

Repository: https://github.com/AI-Efficiency/Awesome-Model-Quantization
Canonical: https://ross.abutalabs.com/products/awesome-model-quantization
License Family: other
Topics: deep-learning, quantization, awesome, model-compression, binarized-neural-networks, binary-network, efficient-deep-learning, lightweight-neural-network, model-acceleration, model-quantization
Last push: 2026-07-10T15:45:57+00:00

## Health v2 (maintenance only)
Score: 73/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 35, longevity 100
- inputs: {"age_days": 2876, "days_push": 54, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2431, forks 242 (observed 2026-08-28T04:06:51.107416+00:00)

## What it is
A curated awesome-list collecting papers, benchmarks, books, and code repositories about neural network and LLM model quantization. It serves as a research reference hub, continuously updated via community pull requests.

## Use cases
- find papers on model quantization
- learn about LLM quantization techniques
- research neural network binarization
- find quantization benchmarks and code
- survey model compression literature
- keep up with efficient deep learning research

## When to choose
- you need a curated starting point for quantization research
- you want links to papers, benchmarks, and implementations in one place
- you are surveying model compression or efficient inference literature

## When to avoid
- you need a runnable quantization tool or library rather than a reading list
- you need production-ready quantization pipelines
- you expect a maintained software package with a license

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, llm-inference, documentation
- domain: deep-learning, large-language-models, machine-learning, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, model-quantization, model-compression, papers, binarized-neural-networks, efficient-inference

## Member repositories
- AI-Efficiency/Awesome-Model-Quantization (main) score 73

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:51.107416+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:31:24.738116+00:00, confidence not recorded.
  - readme: https://github.com/AI-Efficiency/Awesome-Model-Quantization (fetched 2026-08-28T04:06:51.107416+00:00, sha 172c4e639477)
- Data as of 2026-08-30T08:39:29.467469+00:00.