nvidia/model-optimizer
Grade a repo

96

/ 100

GradeA

Exemplary work. Strong across project hygiene, documentation, and engineering.

Top 5% of 5,156 graded repos

A unified library of SOTA model optimization techniques like quantization, distillation, pruning, neural architecture search, speculative decoding, etc. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM, TensorRT, vLLM, etc. to optimize inference speed.

Documentation97
Engineering93
Health100
Python5,359Apache-2.020d ago

Outstanding. A score of 96/100 puts this repo in a very small tier of truly well-engineered projects.

Top fixes

Highest-impact changes first, ranked by point weight

5 to address
  1. 1
    CI/CD14pt

    Add a lint step to catch style issues automatically.

  2. 2
    CI/CD14pt

    Add `tsc --noEmit`, `mypy`, or `cargo check` to catch type errors before they merge.

  3. 3
    Install and run instructions9pt

    Add a .env.example listing all required environment variables so contributors know what to set up.

  4. 4
    Reproducibility6pt

    Add .github/dependabot.yml with at least one package-ecosystem entry, or a renovate.json, so dependencies are updated automatically.

Working through the fixes? Let every push regrade itself.

The free GitHub App rescans this repo on every push and posts the grade as a commit check, so the score climbs without coming back to rescan by hand.

Install the GitHub App

Scorecard

Every check, grouped by category and sorted worst-first

Documentation

97

Install and run instructions9pt90

README documents how to install the project.

Contributing guide5pt97

Contributing guide is detailed and thorough.

README12pt100

README is present.

License6pt100

Licensed under Apache-2.0.

Engineering

93

Reproducibility6pt80

Lockfile present (uv.lock). Installs are reproducible.

CI/CD14pt85

CI is configured (.github/workflows/check_model_hub_orgs.yml).

Tests18pt100

Test files detected (experimental/dms/tests).

Linting and formatting5pt100

Linter or formatter configured ([tool.ruff] / [tool.black] in pyproject.toml).

Issue and PR templates6pt100

Issue or PR templates present.

Project health

100

Dependency manifest6pt100

Dependency manifest found (pyproject.toml).

Repository metadata5pt100

Repository has a description.

Activity5pt100

Actively maintained (pushed within the last month).

Housekeeping3pt100

.gitignore present.

Repository health signals

Activity, community, and responsiveness at scan time

Activity

  • 160 / 370
    Commits (30d / 90d)
  • 754
    Forks
  • 52
    Releaseslatest 1y ago

Community

  • 100% - Good
    Community health
  • 5 bus factor
    authors own >50% of commits
  • 5,359
    Watchers

Responsiveness

  • 10h
    Median issue response
  • 5d 23h
    Median PR merge time
  • 501
    Open issues
Repository files34 root entries
  • .agents
  • .claude
  • .claude-plugin
  • .codex
  • .github
    Good: CI is configured (.github/workflows/check_model_hub_orgs.yml).
    Good: Issue or PR templates present.
  • .gitlab
  • .vscode
  • docs
  • examples
    Good: Environment pinned via examples/onnx_ptq/Dockerfile.
  • experimental
    Good: Test files detected (experimental/dms/tests).
  • modelopt
  • modelopt_recipes
  • plugins
  • tests
  • tools
  • .coderabbit.yaml
  • .dockerignore
  • .gitignore
    Good: .gitignore present.
  • .gitmodules
  • .markdownlint-cli2.yaml
  • .pre-commit-config.yaml
  • AGENTS.md
  • CHANGELOG.rst
  • CLAUDE.md
  • CODE_OF_CONDUCT.md
    Good: Code of conduct present.
  • CONTRIBUTING.md
    Good: Contributing guide is detailed and thorough.
    Good: Contributing guide includes setup/install instructions.
    Issue: Contributing guide lacks a code style section (−8 pts).Fix: Describe your linting/formatting rules and how to run them.
    Good: Contributing guide explains how to run tests.
    Good: Contributing guide describes the PR/review workflow.
    Good: Contributing guide includes code examples.
  • LICENSE
    Good: Licensed under Apache-2.0.
  • LICENSE_HEADER
  • MANIFEST.in
  • noxfile.py
  • pyproject.toml
    Good: Dependency manifest found (pyproject.toml).
  • README.md
    Good: README is present.
    Good: README is well structured with multiple sections.
    Good: README includes screenshots or visuals. Great for first impressions.
    Good: README has code examples.
    Good: README links to a live demo or deployed app.
    Good: README includes status badges.
    Good: README documents how to install the project.
    Good: README documents how to run the project.
  • SECURITY.md
    Good: Security policy present.
  • uv.lock
    Good: Lockfile present (uv.lock). Installs are reproducible.
RepoGrade badge preview

Add this badge to your README

It updates automatically each time the repo is re-graded.

[![RepoGrade](https://www.repo-grade.com/api/badge/nvidia/model-optimizer)](https://www.repo-grade.com/report/nvidia/model-optimizer)

More graded Python repos