Public record
Software health reportschema 0.16.0 · metrics 1.13.0 · 2026-07-21 02:51 UTC

QwenLM / Qwen3-VL

Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

Jupyter NotebookApache-2.0★ 19,630 stars⑂ 1,824 forkssince Aug 2024View on GitHub ↗

QwenLM/Qwen3-VL holds a health index of 45 out of 100, placing it in the At risk band. It scores highest on Community & Adoption (75/100) and lowest on Security (21/100). It was last updated 171 days ago. 2 contributors account for most of its recent work.

45
overall / 100
At risk

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

45
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe 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.

Ownership

QwenOrganization
18,070 followers49 public repossince Aug 2023

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIqwen-vl-utilspoints to another repo — not scored0.0.142,047,57715300 days agolarge-language-modelpytorchqwen-vlvision-language-model

Metrics by category

Vitality

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

24Critical · 22% of overall
How it's scored
9.9/36Push recency — last push 171 days ago
9/36Commit cadence — 13/52 weeks with commits
17.5/18Commit volume — 88 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year88
human_commit_share
days_since_last_push171
active_weeks_last_year13
How it's scored
0/27Ships releases — no releases published
0/36Release recency — no releases
0/27Release cadence — no releases
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count0

Community & Adoption

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

75Good · 18% of overall
How it's scored
60/60Stars — 19,630 stars
25/25Forks — 1,824 forks
10.9/15Watchers — 93 watchers
Inputs used
forks1,824
stars19,630
watchers93
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sustainability & Governance

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

68Moderate · 24% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
11.9/22.5Commit distribution — top contributor authored 47% of commits
13.5/13.5Contributor breadth — 49 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor2
contributors_sampled49
top_contributor_share0.471
How it's scored
36.7/46.8Issue resolution — 79% of issues closed
27.5/38.3PR acceptance — 82/114 decided PRs merged
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs82
open_issues390
closed_issues1,433
issue_closed_ratio0.786
closed_unmerged_prs32
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 18,070 followers of QwenLM
18.3/25Track record — 49 public repos, account ~2 yr old
Inputs used
followers18,070
owner_typeOrganization
is_verified
owner_loginQwenLM
public_repos49
account_age_days1,083

Engineering Quality

Are baseline engineering and documentation practices in place?

34At risk · 20% of overall
How it's scored
0/24CI workflows
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_cino
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

40At risk
How it's scored
30/30README
0/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
0/10Topics
0/10Wiki
Inputs used
topics
has_wikino
homepage
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

21Critical · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestsqwen-vl-utils/pyproject.toml, requirements_web_demo.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages8
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:qwen-vl-utils@0.0.14 runtime dependency closure — what installing the published package pulls in — 8 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.

27Critical · 0% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practice — no data
0/8Automated maintenance — no data
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
0/45Type-checkable code — Jupyter Notebook without a type-check config
55/55Manageable file sizes — 0/32 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes27,447
source_files_sampled32
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files

Key facts

19,630GitHub stars
49contributors
88commits, last 12 months
171days since last push
0releases
2bus factor
390open issues
PyPIpackage ecosystems

Data collection warnings

  • pypi package 'qwen-vl-utils' points at a different repository (https://github.com/QwenLM/Qwen2-VL.git); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (2026/07/21 02:51:04 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Direct dependencies 4
RegistryPackageVersion constraintManifest
PyPIrequestsqwen-vl-utils/pyproject.toml
PyPIpillowqwen-vl-utils/pyproject.toml
PyPIavqwen-vl-utils/pyproject.toml
PyPIpackagingqwen-vl-utils/pyproject.toml
All dependencies 26

Full resolved dependency set from the GitHub dependency graph: 4 direct and 22 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIavdirect
PyPIpackagingdirect
PyPIpillowdirect
PyPIrequestsdirect
PyPIaccelerateindirect
PyPIdatasetsindirect
PyPIdecordindirect
PyPIflash-attnindirect
PyPIgradio5.49.1indirect
PyPIgradio-client1.13.3indirect
PyPIlatex2sympy2indirect
PyPInumpyindirect
PyPIopenpyxlindirect
PyPIpandasindirect
PyPIpycocotoolsindirect
PyPIpysubs2indirect
PyPIqwen-vl-utilsindirect
PyPIsentencepieceindirect
PyPItabulateindirect
PyPItorchindirect
PyPItorchvisionindirect
PyPItqdmindirect
PyPItransformersindirect
PyPItransformers-stream-generator0.0.5indirect
PyPIvalidatorsindirect
PyPIvllmindirect
Dependency advisories 0

Installing pypi:qwen-vl-utils@0.0.14 pulls in 8 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

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
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        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "at_risk",
            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "demonstrated_agent_practice",
                    "automated_maintenance"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 31,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [],
              "has_dockerfile": false,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": false,
              "typecheck_configs": [],
              "agent_commit_share": null,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": null
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "OpenSSF Scorecard unavailable",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "moderate",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 55,
            "inputs": {
              "primary_language": "Jupyter Notebook",
              "largest_source_bytes": 27447,
              "source_files_sampled": 32,
              "oversized_source_files": 0
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Jupyter Notebook without a type-check config",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_typecheck_config_language",
                    "params": {
                      "language": "Jupyter Notebook"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "0/32 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 32,
                      "oversized": 0
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "notebooks"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "pypi package 'qwen-vl-utils' points at a different repository (https://github.com/QwenLM/Qwen2-VL.git); excluded from ecosystem scoring",
    "OpenSSF Scorecard did not return a usable result (2026/07/21 02:51:04 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-21T02:51:35.729641Z",
  "schema_version": "0.16.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/q/QwenLM/Qwen3-VL.svg",
  "full_name": "QwenLM/Qwen3-VL",
  "license_state": "standard",
  "license_spdx": "Apache-2.0"
}

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 v1.13.0, schema v0.16.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.