Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-10 12:37 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.

Python · CudaCustom license★ 1,253 stars⑂ 206 forkssince Jun 2024View on GitHub ↗
KindLibraryhow this is determined
#gptq#optimum#peft#quantization#sglang#transformers#vllm

ModelCloud/GPTQModel holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Vitality (96/100) and lowest on Security (61/100). It was last updated today. 2 contributors account for most of its recent work.

91
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

91
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

ModelCloud.aiOrganization
63 followers23 public repossince Jun 2024

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIGPTQModel7.4.028,198622 days agogptqawqqqqautogptqautoawqeoragarquantizationlarge-language-modelstransformersllmmoecompression

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

96Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 1,098 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,098
human_commit_share0.99
days_since_last_push0
active_weeks_last_year52

Release discipline

90Excellent
How it's scored
27/27Ships releases — 74 releases published
36/36Release recency — latest release 2 days ago
27/27Release cadence — a release every ~17.6 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count74
latest_release_tagv7.4.0
releases_from_tagsno
days_since_latest_release2
mean_days_between_releases17.6

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

66Good · 17% of overall

Popularity & adoption

73Good
How it's scored
50.2/60Stars — 1,253 stars
19.3/25Forks — 206 forks
3.3/15Watchers — 5 watchers
Inputs used
forks206
stars1,253
watchers5
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

51Moderate
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

74Good
How it's scored
59.3/80Monthly downloads — 28,198 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesGPTQModel
dependents—
ecosystemspypi
total_downloads—
monthly_downloads28,198
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

76Good · 23% of overall

Maintainer resilience (bus factor)

64Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
14.9/22.5Commit distribution — top contributor authored 34% of commits
13.5/13.5Contributor breadth — 95 contributors
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor2
contributors_sampled95
top_contributor_share0.336

Issue & PR responsiveness

88Excellent
How it's scored
37.3/42Issue resolution — 89% of issues closed
28.4/30PR acceptance — 2,547/2,689 decided PRs merged
13/13Newcomer PR acceptance — 7/7 first-time contributors' PRs merged in 30d
9/15OpenSSF Scorecard: Code-Review — Found 19/30 approved changesets -- score normalized to 6
Inputs used
merged_prs2,547
open_issues41
closed_issues323
prs_merged_7d18
prs_decided_7d21
prs_merged_30d49
prs_decided_30d52
issue_closed_ratio0.887
closed_unmerged_prs142
first_time_authors_30d5
first_time_prs_merged_30d7
first_time_prs_decided_30d7

Ownership & stewardship

58Moderate
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
13/25Owner reach — 63 followers of ModelCloud
14.5/25Track record — 23 public repos, account ~2 yr old
Inputs used
followers63
owner_typeOrganization
is_verifiedno
owner_loginModelCloud
public_repos23
account_age_days818

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 2 days ago
20/20Version history — 62 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesGPTQModel
ecosystemspypi
any_deprecatedno
min_days_since_publish2

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall

Engineering practices

80Excellent
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
16/16Linter config — ruff.toml
0/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 25 out of 30 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://x.com/Qubitium
10/10Repository description
10/10Topics — 7 topics
0/10Wiki
Inputs used
topicsgptq, optimum, peft, quantization, sglang, transformers, vllm
has_wikino
homepagehttps://x.com/Qubitium
docs_sitehttps://x.com/Qubitium
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

61Moderate · 16% of overall

Security posture

56Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2/2.5CI-Tests — 25 out of 30 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
4.5/7.5Code-Review — Found 19/30 approved changesets -- score normalized to 6
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
3.5/5SAST — SAST tool is not run on all commits -- score normalized to 7
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
6.8/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 34 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5.6
Excluded from scoring (no data or not applicable): Packaging. Remaining weights renormalized.

Dependency advisories

80Excellent
How it's scored
15.1/35Direct dependencies free of known advisories — 1 affected: accelerate 1.14.0 (high 7.1)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages85
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages1
Matched the pypi:GPTQModel@7.4.0 runtime dependency closure — what installing the published package pulls in — 85 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

64Moderate · 4% of overall

Agent context & guidance

85Excellent
How it's scored
45/45Agent instructions — AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 97 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share0.98
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes1,039

Verify loop (build / test / typecheck)

51Moderate
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — ruff.toml
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice — 8 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 1 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.08
toolchain_manifests—
dependency_bot_commit_share0.01

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
53.6/55Manageable file sizes — 24/964 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes431,195
source_files_sampled964
oversized_source_files24

Key facts

1,253GitHub stars
95contributors
1,098commits, last 12 months
0days since last push
74releases
2bus factor
41open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 206 ⇿
0Stars
206Forks
74Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0408012016020024020242024-062025-072026-09
Major 6Minor 22Patch 46

Each point covers 3 days.

OpenSSF Scorecard 5.6 / 10
5.6aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-10 12:37 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
8CI-Tests25 out of 30 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6Code-ReviewFound 19/30 approved changesets -- score normalized to 6
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
7SASTSAST tool is not run on all commits -- score normalized to 7
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
9Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities34 existing vulnerabilities detected
All dependencies 43

Full resolved dependency set from the GitHub dependency graph: 0 direct and 43 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaccelerate—indirect
PyPIbitblas0.1.0.post1indirect
PyPIbitsandbytes—indirect
PyPIdatasets—indirect
PyPIdefuser—indirect
PyPIdevice-smi—indirect
PyPIdill—indirect
PyPIevalution—indirect
PyPIfilelock—indirect
PyPIflashinfer-python—indirect
PyPIjinja2—indirect
PyPIlogbar—indirect
PyPImaturin—indirect
PyPImlx-lm—indirect
PyPIninja—indirect
PyPInumpy2.2.6indirect
PyPInvidia-cublas—indirect
PyPInvidia-cublas-cu1212.9.1.4indirect
PyPInvidia-cuda-runtime—indirect
PyPInvidia-cuda-runtime-cu1212.9.79indirect
PyPInvidia-cusolver—indirect
PyPInvidia-cusolver-cu1211.7.5.82indirect
PyPInvidia-cusparse—indirect
PyPInvidia-cusparse-cu1212.5.10.65indirect
PyPIoptimum—indirect
PyPIpackaging—indirect
PyPIpillow—indirect
PyPIprotobuf—indirect
PyPIpyarrow—indirect
PyPIpypcre—indirect
PyPIpytest—indirect
PyPIpytest-timeout—indirect
PyPIruff0.14.2indirect
PyPIsafetensors—indirect
PyPIsetuptools—indirect
PyPIsglang—indirect
PyPIthreadpoolctl—indirect
PyPItokenicer—indirect
PyPItorch—indirect
PyPItorchao—indirect
PyPItransformers—indirect
PyPItriton—indirect
PyPIvllm—indirect
Dependency advisories 1

Installing pypi:GPTQModel@7.4.0 pulls in 85 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
accelerate1.14.0directhigh2—

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Related records

Inspected repositories sharing catalogue tags or ecosystems with ModelCloud/GPTQModel. Read them side by side →

95intel/auto-roundA SOTA quantization toolkit for high-accuracy low-bit LLM inference|简洁且高效的量化工具包Exceptional98vllm-project/llm-compressorTransformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLMExceptional98openvinotoolkit/nncfNeural Network Compression Framework for enhanced OpenVINO™ inferenceExceptional97xorbitsai/inferenceSwap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.Exceptional96intel/neural-compressorSOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX RuntimeExceptional94modelscope/ms-swiftUse PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Gemma4, Llava, Phi4, ...) (AAAI 2025).Exceptional

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.