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Software-GesundheitsberichtSchema 0.12.0 · Metriken 1.13.0 · 2026-07-17 22:01 UTC
ModelCloud / GPTQModel
LLM model quantization (compression) toolkit with HW acceleration support for Nvidia, AMD, Intel GPU and Intel/AMD/Apple CPU via HF, vLLM, and SGLang.
ModelCloud/GPTQModel erreicht einen Gesundheitsindex von 76 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (95/100) ab, am schwächsten bei AI Readiness (30/100). Zuletzt vor 1 Tag aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.
76
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
76
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Eigentümerschaft
ModelCloud.aiOrganisation
63 Follower18 öffentliche Reposseit Juni 2024
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | GPTQModel | 7.1.0 | - | 55 | vor 39 Tagen | gptqawqqqqautogptqautoawqeoragarquantizationlarge-language-modelstransformersllmmoecompression |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
95
Entwicklungsaktivität
98Exzellent
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 1 Tagen |
| 33.9/36 | Commit-Rhythmus — 49/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 1.087 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 16 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 1.087 |
| human_commit_share | — |
| days_since_last_push | 1 |
| active_weeks_last_year | 49 |
Release-Disziplin
90Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 68 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 14 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 22,2 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases. |
Verwendete Eingangsdaten
| releases_count | 68 |
| latest_release_tag | v7.2.0 |
| releases_from_tags | nein |
| days_since_latest_release | 14 |
| mean_days_between_releases | 22,2 |
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
62
Wie die Bewertung erfolgt
| 50/60 | Stars — 1.207 Stars |
| 19/25 | Forks — 192 Forks |
| 3.3/15 | Watcher — 5 Watcher |
Verwendete Eingangsdaten
| forks | 192 |
| stars | 1.207 |
| watchers | 5 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
51Mittel
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 16.9/22.5 | Lizenz — Lizenzdatei vorhanden, keine anerkannte Lizenz |
| 0/18 | CONTRIBUTING-Leitfaden |
| 0/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | nein |
| has_issue_template | nein |
| has_code_of_conduct | nein |
| has_pull_request_template | ja |
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
76
Wie die Bewertung erfolgt
| 25.2/54 | Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab |
| 15.1/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 33 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 90 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10 |
Verwendete Eingangsdaten
| bus_factor | 2 |
| contributors_sampled | 90 |
| top_contributor_share | 0,327 |
Reaktionsfähigkeit bei Issues & PRs
91Exzellent
Wie die Bewertung erfolgt
| 41.1/46.8 | Issue-Lösungsquote — 88 % der Issues geschlossen |
| 36.3/38.3 | PR-Annahme — 2.445/2.579 entschiedene PRs gemergt |
| 13.5/15 | OpenSSF Scorecard: Code-Review — Found 27/29 approved changesets -- score normalized to 9 |
Verwendete Eingangsdaten
| merged_prs | 2.445 |
| open_issues | 43 |
| closed_issues | 315 |
| issue_closed_ratio | 0,88 |
| closed_unmerged_prs | 134 |
Eigentümerschaft & Trägerschaft
56Mittel
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 13/25 | Reichweite des Inhabers — 63 Follower von ModelCloud |
| 13.5/25 | Kontohistorie — 18 öffentliche Repos, Kontoalter ca. 2 Jahre |
Verwendete Eingangsdaten
| followers | 63 |
| owner_type | Organization |
| is_verified | — |
| owner_login | ModelCloud |
| public_repos | 18 |
| account_age_days | 763 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 39 Tagen |
| 20/20 | Versionshistorie — 55 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | GPTQModel |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 39 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
84
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 6 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration — ruff.toml |
| 0/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 16/20 | OpenSSF Scorecard: CI-Tests — 25 out of 30 merged PRs checked by a CI test -- score normalized to 8 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | nein |
Dokumentation
90Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://x.com/Qubitium |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 7 Topics |
| 0/10 | Wiki |
Verwendete Eingangsdaten
| topics | gptq, optimum, peft, quantization, sglang, transformers, vllm |
| has_wiki | nein |
| homepage | https://x.com/Qubitium |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
58
Sicherheitslage
58Mittel
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 2.2/7.5 | Branch-Protection — branch protection is not maximal on development and all release branches |
| 2/2.5 | CI-Tests — 25 out of 30 merged PRs checked by a CI test -- score normalized to 8 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 6.8/7.5 | Code-Review — Found 27/29 approved changesets -- score normalized to 9 |
| 2.5/2.5 | Contributors — project has 3 contributing companies or organizations -- score normalized to 10 |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.2/2.5 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 16 issue activity found in the last 90 days -- score normalized to 10 |
| 0/5 | Packaging — keine Daten |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 3.5/5 | SAST — SAST tool is not run on all commits -- score normalized to 7 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — Project has not signed or included provenance with any releases. |
| 6.8/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 0/7.5 | Vulnerabilities — 28 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 17 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 1 |
| scorecard_aggregate | 5,8 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): packaging. Die verbleibenden Gewichte wurden renormalisiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
30
AI-Agentenkontext & Leitlinien
1Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 0/40 | Lesbare Commit-Historie — keine Daten |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | — |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
AI-Verify-Loop (Build / Test / Typprüfung)
42Gefährdet
Wie die Bewertung erfolgt
| 0/18 | Bootstrap mit einem Befehl |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration — ruff.toml |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 0/10 | Belegte Agentenpraxis — keine Daten |
| 5/8 | Automatisierte Wartung — Abhängigkeits-Automatisierung konfiguriert, in den erfassten Commits nicht beobachtet |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | — |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis. Die verbleibenden Gewichte wurden renormalisiert.
AI-Code-Lesbarkeit für Modelle
54Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 53.6/55 | Handhabbare Dateigrößen — 21/820 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 429.650 |
| source_files_sampled | 820 |
| oversized_source_files | 21 |
Eckdaten
1.207GitHub-Sterne
90Mitwirkende
1.087Commits, letzte 12 Monate
1Tage seit letztem Push
68Releases
2Bus-Faktor
43offene Issues
PyPIPaket-Ökosysteme
Weitere Details
OpenSSF Scorecard 5.8 / 10
5.8Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| 3 | Branch-Protection | branch protection is not maximal on development and all release branches |
| 8 | CI-Tests | 25 out of 30 merged PRs checked by a CI test -- score normalized to 8 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 9 | Code-Review | Found 27/29 approved changesets -- score normalized to 9 |
| 10 | Contributors | project has 3 contributing companies or organizations -- score normalized to 10 |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 9 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 16 issue activity found in the last 90 days -- score normalized to 10 |
| k. A. | Packaging | packaging workflow not detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 7 | SAST | SAST tool is not run on all commits -- score normalized to 7 |
| 0 | Security-Policy | security policy file not detected |
| 0 | Signed-Releases | Project has not signed or included provenance with any releases. |
| 9 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 0 | Vulnerabilities | 28 existing vulnerabilities detected |
Alle Abhängigkeiten 41
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 41 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | accelerate | — | indirekt |
| PyPI | bitblas | 0.1.0.post1 | indirekt |
| PyPI | bitsandbytes | — | indirekt |
| PyPI | datasets | — | indirekt |
| PyPI | defuser | — | indirekt |
| PyPI | device-smi | — | indirekt |
| PyPI | dill | — | indirekt |
| PyPI | flashinfer-python | — | indirekt |
| PyPI | jinja2 | — | indirekt |
| PyPI | logbar | — | indirekt |
| PyPI | maturin | — | indirekt |
| PyPI | mlx-lm | — | indirekt |
| PyPI | ninja | — | indirekt |
| PyPI | numpy | 2.2.6 | indirekt |
| PyPI | nvidia-cublas | — | indirekt |
| PyPI | nvidia-cublas-cu12 | 12.9.1.4 | indirekt |
| PyPI | nvidia-cuda-runtime | — | indirekt |
| PyPI | nvidia-cuda-runtime-cu12 | 12.9.79 | indirekt |
| PyPI | nvidia-cusolver | — | indirekt |
| PyPI | nvidia-cusolver-cu12 | 11.7.5.82 | indirekt |
| PyPI | nvidia-cusparse | — | indirekt |
| PyPI | nvidia-cusparse-cu12 | 12.5.10.65 | indirekt |
| PyPI | optimum | — | indirekt |
| PyPI | packaging | — | indirekt |
| PyPI | pillow | — | indirekt |
| PyPI | protobuf | — | indirekt |
| PyPI | pyarrow | — | indirekt |
| PyPI | pypcre | — | indirekt |
| PyPI | pytest | — | indirekt |
| PyPI | pytest-timeout | — | indirekt |
| PyPI | ruff | 0.14.2 | indirekt |
| PyPI | safetensors | — | indirekt |
| PyPI | setuptools | — | indirekt |
| PyPI | sglang | — | indirekt |
| PyPI | threadpoolctl | — | indirekt |
| PyPI | tokenicer | — | indirekt |
| PyPI | torch | — | indirekt |
| PyPI | torchao | — | indirekt |
| PyPI | transformers | — | indirekt |
| PyPI | triton | — | indirekt |
| PyPI | vllm | — | indirekt |
JSON-Rohbericht maschinenlesbar
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{
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"points": 25,
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{
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{
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{
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"key": "engineering_practices",
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"note": null,
"notes": [],
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{
"key": "tests_present",
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"points": 24,
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{
"key": "linter_config",
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},
{
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{
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{
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{
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{
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{
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},
{
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{
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{
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{
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{
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{
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"components": [
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{
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}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.12.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.