Public record
Software health reportschema 0.32.0 · metrics 2.10.0 · 2026-08-20 06:39 UTC

FedericoTs / quantprobe

Run a 110B on a 2016 PC with 16 GB RAM. Know your tok/s before you download. Placement beats budget: predicts speed + memory fit for any GGUF on your exact hardware, self-calibrates, emits the exact llama.cpp command — or 'quantprobe auto' does it all. Falsification-tested laws; misses published at full size. pip install quantprobe

PythonMIT★ 86 stars⑂ 8 forkssince Jul 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

FedericoTs/quantprobe holds a health index of 63 out of 100, placing it in the Moderate band. It scores highest on Vitality (78/100) and lowest on Security (25/100). It was last updated today. A single contributor accounts for most of its recent work.

63
overall / 100
Moderate

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.

63
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 59 is calibrated to 63 on the published index scale (record calibration 2026-08-02).

Ownership

FedeSPersonal account
4 followers8 public repossince Aug 2018

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIquantprobe1.32.06,494530 days agollmquantizationllama.cppggufinferencemoe

Metrics by category

Vitality

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

78Good · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
3.5/36Commit cadence5/52 weeks with commits
18/18Commit volume370 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year370
human_commit_share1
days_since_last_push0
active_weeks_last_year5

Release discipline

100Exceptional
How it's scored
27/27Ships releases25 releases published
36/36Release recencylatest release 2 days ago
27/27Release cadencea release every ~2.2 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count25
latest_release_tagv1.28.2
releases_from_tagsno
days_since_latest_release2
mean_days_between_releases2.2

Community & Adoption

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

56Moderate · 17% of overall
How it's scored
31.3/60Stars86 stars
7/25Forks8 forks
0/15Watchers0 watchers
Inputs used
forks8
stars86
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges5
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
50.8/80Monthly downloads6,494 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagesquantprobe
dependents
ecosystemspypi
total_downloads
monthly_downloads6,494
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?

53Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0.1/22.5Commit distributiontop contributor authored 100% of commits
2.7/13.5Contributor breadth2 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.997
How it's scored
21/42Issue resolution50% of issues closed
30/30PR acceptance1/1 decided PRs merged
13/13Newcomer PR acceptance1/1 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs1
open_issues1
closed_issues1
prs_merged_7d1
prs_decided_7d1
prs_merged_30d1
prs_decided_30d1
issue_closed_ratio0.5
closed_unmerged_prs0
first_time_authors_30d1
first_time_prs_merged_30d1
first_time_prs_decided_30d1
How it's scored
10/30Ownership backingpersonal (user) account
0/20Verified domainnot applicable to user accounts
5/25Owner reach4 followers of FedericoTs
18.9/25Track record8 public repos, account ~8 yr old
Inputs used
followers4
owner_typeUser
is_verified
owner_loginFedericoTs
public_repos8
account_age_days2,935
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 0 days ago
20/20Version history53 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesquantprobe
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

76Good · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://federicots.github.io/quantprobe/
10/10Repository description
10/10Topics11 topics
10/10Wiki
Inputs used
topicsgguf, inference, llama-cpp, llm, moe, quantization, local-llm, benchmark, performance, hardware-detection, speculative-decoding
has_wikiyes
homepagehttps://federicots.github.io/quantprobe/
docs_sitehttps://federicots.github.io/quantprobe/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

25At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
25/25Dependency lockfilesCargo.lock
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesCargo.lock
manifestscmcore/Cargo.toml, pyproject.toml, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share1
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
10/10Reproducible environmentlockfile
10/10Demonstrated agent practice98 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfilesCargo.lock
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0.98
toolchain_manifestscmcore/Cargo.toml
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
53.6/55Manageable file sizes7/269 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes278,192
source_files_sampled269
oversized_source_files7
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesrecipes
Inputs used
example_dirsrecipes
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

86GitHub stars
2contributors
370commits, last 12 months
0days since last push
25releases
1bus factor
1open issues
crates.io, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch crates package 'cmcore' from its registry
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • deps.dev does not index pypi:quantprobe@1.32.0; advisories assessed against the repository dependency graph instead
  • OpenSSF Scorecard did not return a usable result (2026/08/20 06:38:03 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 8 ⇿
0Stars
8Forks
21Releases

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.

2345678822026-072026-082026-08
Major 0Minor 14Patch 7
Direct dependencies 2
RegistryPackageVersion constraintManifest
PyPIgguf>=0.9pyproject.toml
PyPIrequests>=2.28pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

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.

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 v0.32.0 — full methodology · metrics wiki.

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