Public recordModerate
Software health reportschema 0.26.0 · metrics 1.13.0 · 2026-07-22 19:51 UTC
jjang-ai / jangq
JANG — GGUF for MLX. YOU MUST USE JANG_Q RUNTIME. Adaptive Mixed-Precision Quantization + Runtime for Apple Silicon
jjang-ai/jangq holds a health index of 54 out of 100, placing it in the Moderate band. It scores highest on Vitality (82/100) and lowest on Security (26/100). It was last updated today. A single contributor accounts for most of its recent work.
54
overall / 100Software 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.
54
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)
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
Eric Jinho JangPersonal account
217 followers8 public repossince Jul 2023Osaurus.ai
This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.
Package ecosystems
| Registry | Package | Version | Downloads / mo | Versions | Last publish | Tags |
|---|---|---|---|---|---|---|
| PyPI | jang | 2.5.33 | - | 43 | 0 days ago | quantizationllmapple-siliconmetalmlxjangmoemixed-precision |
Metrics by category
Vitality
Is the project alive — is code being written and are releases shipping?
82
Development activity
76Good
How it's scored
| 36/36 | Push recency — last push 0 days ago |
| 11.8/36 | Commit cadence — 17/52 weeks with commits |
| 18/18 | Commit volume — 565 commits in the last year |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
Inputs used
| commits_last_year | 565 |
| human_commit_share | 1 |
| days_since_last_push | 0 |
| active_weeks_last_year | 17 |
Release discipline
90Excellent
How it's scored
| 27/27 | Ships releases — 9 releases published |
| 36/36 | Release recency — latest release 0 days ago |
| 27/27 | Release cadence — a release every ~15.4 days |
| 0/10 | OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases. |
Inputs used
| releases_count | 9 |
| latest_release_tag | v2.5.33 |
| releases_from_tags | no |
| days_since_latest_release | 0 |
| mean_days_between_releases | 15.4 |
Community & Adoption
Does the project have users, downloads, attention, and a welcoming setup for contributors?
39
Popularity & adoption
51Moderate
How it's scored
| 37.8/60 | Stars — 214 stars |
| 11.8/25 | Forks — 27 forks |
| 1.7/15 | Watchers — 3 watchers |
Inputs used
| forks | 27 |
| stars | 214 |
| watchers | 3 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community health
25Critical
How it's scored
| 22.5/22.5 | README |
| 0/22.5 | License — no license file detected |
| 0/18 | CONTRIBUTING guide |
| 0/13.5 | Code of conduct |
| 0/7.2 | Issue template |
| 0/6.3 | PR template |
Inputs used
| has_readme | yes |
| has_license | no |
| has_contributing | no |
| has_issue_template | no |
| has_code_of_conduct | no |
| has_pull_request_template | no |
Sustainability & Governance
Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?
39
Maintainer resilience (bus factor)
13Critical
How it's scored
| 9/54 | Bus factor — 1 contributor(s) cover half of all commits |
| 0/22.5 | Commit distribution — top contributor authored 100% of commits |
| 1.4/13.5 | Contributor breadth — 1 contributors |
| 3/10 | OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3 |
Inputs used
| bus_factor | 1 |
| contributors_sampled | 1 |
| top_contributor_share | 1 |
Issue & PR responsiveness
12Critical
How it's scored
| 4.7/46.8 | Issue resolution — 10% of issues closed |
| 7.7/38.3 | PR acceptance — 1/5 decided PRs merged |
| 0/15 | OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0 |
Inputs used
| merged_prs | 1 |
| open_issues | 9 |
| closed_issues | 1 |
| issue_closed_ratio | 0.1 |
| closed_unmerged_prs | 4 |
Ownership & stewardship
50Moderate
How it's scored
| 10/30 | Ownership backing — personal (user) account |
| 0/20 | Verified domain — not applicable to user accounts |
| 16.8/25 | Owner reach — 217 followers of jjang-ai |
| 13.1/25 | Track record — 8 public repos, account ~3 yr old |
Inputs used
| followers | 217 |
| owner_type | User |
| is_verified | — |
| owner_login | jjang-ai |
| public_repos | 8 |
| account_age_days | 1,115 |
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.
Package maintenance
100Excellent
How it's scored
| 25/25 | Published & resolvable — 1 package(s) on pypi |
| 35/35 | Publish recency — latest publish 0 days ago |
| 20/20 | Version history — 43 published versions |
| 20/20 | Not deprecated — active, not deprecated or yanked |
Inputs used
| packages | jang |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 0 |
Engineering Quality
Are baseline engineering and documentation practices in place?
76
Engineering practices
60Moderate
How it's scored
| 24/24 | CI workflows — 1 workflow(s) |
| 24/24 | Tests present |
| 0/16 | Linter config |
| 0/9.6 | Pre-commit hooks |
| 0/6.4 | .editorconfig |
| 0/20 | OpenSSF Scorecard: CI-Tests — no data |
Inputs used
| has_ci | yes |
| has_tests | yes |
| has_editorconfig | no |
| has_linter_config | no |
| has_precommit_config | no |
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.
Documentation
100Excellent
How it's scored
| 30/30 | README |
| 25/25 | Documentation directory |
| 15/15 | Documentation / homepage site — https://jangq.ai/ |
| 10/10 | Repository description |
| 10/10 | Topics — 10 topics |
| 10/10 | Wiki |
Inputs used
| topics | gguf, llamacpp, llm, mlx, mlxllm, quantization, apple-silicon, jang-quantization, omlx, omlx-alternative |
| has_wiki | yes |
| homepage | https://jangq.ai/ |
| has_readme | yes |
| has_docs_dir | yes |
| has_description | yes |
Security
Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?
26
Security posture
26Critical
How it's scored
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — branch protection not enabled on development/release branches |
| 0/2.5 | CI-Tests — no data |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 0/7.5 | Code-Review — Found 0/30 approved changesets -- score normalized to 0 |
| 0.8/2.5 | Contributors — project has 1 contributing companies or organizations -- score normalized to 3 |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 0/7.5 | Dependency-Update-Tool — no update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 0/2.5 | License — license file not detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| 0/5 | Packaging — no data |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 0/5 | SAST — no SAST tool detected |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — Project has not signed or included provenance with any releases. |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 0/7.5 | Vulnerabilities — 34 existing vulnerabilities detected |
Inputs used
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 2.6 |
Excluded from scoring (no data or not applicable): ci_tests, packaging. Remaining weights renormalized.
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.
34
Agent context & guidance
24Critical
How it's scored
| 0/45 | Agent instructions — no CLAUDE.md / AGENTS.md / editor rules |
| 0/15 | Machine-readable docs (llms.txt) |
| 24.5/40 | Legible commit history — 46 of 100 human commits state their intent (structured subject or explanatory body) |
Inputs used
| has_llms_txt | no |
| legible_history_share | 0.46 |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
How it's scored
| 0/18 | One-command bootstrap |
| 22/22 | Automated tests |
| 0/11 | Lint / format config |
| 0/11 | Static type checking |
| 10/10 | Reproducible environment — lockfile |
| 0/10 | Demonstrated agent practice — no agent-authored commits among the last 100 |
| 0/8 | Automated maintenance — no automated dependency updates observed |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Inputs used
| has_nix | no |
| has_tests | yes |
| lockfiles | uv.lock |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | — |
| has_devcontainer | no |
| has_linter_config | no |
| typecheck_configs | — |
| agent_commit_share | 0 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0 |
Code legibility for models
54Moderate
How it's scored
| 0/45 | Type-checkable code — Python without a type-check config |
| 54.5/55 | Manageable file sizes — 5/589 source files over 60KB |
Inputs used
| primary_language | Python |
| largest_source_bytes | 99,406 |
| source_files_sampled | 589 |
| oversized_source_files | 5 |
Machine-readable interfaces
40At risk
How it's scored
| 0/40 | API schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP server |
| 40/40 | Runnable examples — examples |
Inputs used
| example_dirs | examples |
| has_mcp_signal | no |
| api_schema_files | — |
Key facts
214GitHub stars
1contributors
565commits, last 12 months
0days since last push
9releases
1bus factor
9open issues
PyPIpackage ecosystems
Data collection warnings
- Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
- Could not fetch pypi package 'jang-ralph-runner' from its registry
- GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
- deps.dev does not index pypi:jang@2.5.33; advisories assessed against the repository dependency graph instead
More detail
Star and fork history 0 ★ / 27 ⇿
0Stars
27Forks
8Releases
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.
Major 0Minor 1Patch 5
OpenSSF Scorecard 2.6 / 10
2.6aggregate
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).
| 10 | Binary-Artifacts | no binaries found in the repo |
| 0 | Branch-Protection | branch protection not enabled on development/release branches |
| n/a | CI-Tests | no pull request found |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 0 | Code-Review | Found 0/30 approved changesets -- score normalized to 0 |
| 3 | Contributors | project has 1 contributing companies or organizations -- score normalized to 3 |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 0 | Dependency-Update-Tool | no update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 0 | License | license file not detected |
| 10 | Maintained | 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10 |
| n/a | Packaging | packaging workflow not detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 0 | SAST | no SAST tool detected |
| 0 | Security-Policy | security policy file not detected |
| 0 | Signed-Releases | Project has not signed or included provenance with any releases. |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 0 | Vulnerabilities | 34 existing vulnerabilities detected |
Direct dependencies 8
| Registry | Package | Version constraint | Manifest |
|---|---|---|---|
| PyPI | safetensors | >=0.4 | jang-tools/pyproject.toml |
| PyPI | numpy | >=1.24 | jang-tools/pyproject.toml |
| PyPI | tqdm | >=4.60 | jang-tools/pyproject.toml |
| PyPI | huggingface_hub | >=0.20 | jang-tools/pyproject.toml |
| PyPI | jinja2 | >=3.1 | jang-tools/pyproject.toml |
| PyPI | ruamel.yaml | >=0.18 | ralph_runner/pyproject.toml |
| PyPI | huggingface_hub | >=0.20 | ralph_runner/pyproject.toml |
| PyPI | rich | >=13.0 | ralph_runner/pyproject.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
{
"data": {
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"kind": "minor",
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{
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"kind": "patch",
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"recent_commits": [
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"body": null,
"is_bot": false,
"headline": "fix(laguna): publish mixed-affine runtime contract",
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"headline": "fix(jang): validate storage widths and clarify Laguna AWQ",
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"headline": "docs(laguna): record storage-width review fix",
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"headline": "fix(jang): keep one-bit storage out of allocation",
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"body": "NOTES.md: 10-point inference checklist (bf16 stream, wired limit,\nper-layer-type masks, keep=0, layer-indexed attention, softplus gate\nwidth branch, router math, eos [2,24]/no-double-BOS, thinking-on default,\nper-module quant bits), session changelog, verification protocol with\ninterpretation nuance\n[…]\n-forced pre/post-window agreement is the real gate).\npython_example.py: smoke / chat / teacher-forced parity drivers.\nSWIFT.md: vmlx-swift port checklist ordered by the audited M.1\nlayer-indexing bug.",
"is_bot": false,
"headline": "docs: laguna-s21 runtime notes, examples, Swift port checklist",
"author_name": "Jinho Jang",
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"body": "Capture runs the REAL jang_tools.laguna.model.LagunaLayer in MLX — not a\nhand-rolled torch forward — so the capture math is the verified runtime\nmath by construction (per-layer heads, dual RoPE/partial rotary, softplus\ngating, sigmoid+bias routing). Streams the 235 GB source with a 2-shard\nLRU (~10 \n[…]\nsured on S-2.1 (corpus_v3 8x512): absmean alpha=0.25 wins at BOTH\n2-bit (+2.4% reconstruction vs the alpha=0 no-AWQ baseline) and 4-bit\n(+2.9%), 0/144384 inert channels. Shipped bundles are AWQ-based.",
"is_bot": false,
"headline": "laguna: AWQ capture (MLX layer-streaming) + cross-family awq_search fix",
"author_name": "Jinho Jang",
"author_login": "jjang-ai",
"committed_at": "2026-07-22T04:00:44Z",
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"body": "poolside/Laguna-S-2.1 (118B-A8B, 48 layers, 256 routed top-10 + shared,\ndense L0, hybrid 12 full/36 SWA with per-layer head counts 48/72).\nProfiles: JANG_2L = the proven M.1 recipe byte-for-byte (routed 2/2/3,\nattn+g_proj 8, shared/dense/embed 6, lm_head 8, gs64), JANG_3L 3/3/4,\nJANG_4M 4/4/4 with s\n[…]\ncoherent) with a loud warning at <=3-bit;\n--awq folds captured scales into routed+shared gate/up and the router gate\nwith the inverse on post_attention_layernorm, keeping routing logits\nbit-identical.",
"is_bot": false,
"headline": "laguna: S-2.1 all-affine JANG converter + FAMILY_MAP registration",
"author_name": "Jinho Jang",
"author_login": "jjang-ai",
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"body_truncated": true,
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{
"oid": "39bf08514a60d756c8fd239ebd9597501f677fd2",
"body": "fp16 stream (bundle sidecar dtype) NaN'd S-2.1-JANG_4M: the residual\ngrows ~4 -> 942k across 48 layers and the L46 MoE intermediate overflows\nfp16's 65504 ceiling -> inf -> NaN logits -> argmax 0 -> UNK spam. The\nsource is torch_dtype=bfloat16; cast all fp16 params to bf16 at load so\nQuantizedLinear\n[…]\ned under decode —\n4M measured 14.0 -> 32.6 tok/s wired; 2L was silently throttled too\n(31.9 -> 48.3). Auto-stamp min(bundle*1.2 + 8GB, 118GB); the 118 GB\nceiling is Ornith-proven on the 128 GB M5 Max.",
"is_bot": false,
"headline": "laguna: bf16 activation stream + wired-limit auto-stamp",
"author_name": "Jinho Jang",
"author_login": "jjang-ai",
"committed_at": "2026-07-22T04:00:44Z",
"body_truncated": true,
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},
{
"oid": "ddb5639232b0c553725ac8603cdac9982c9eb3f8",
"body": "Two hybrid-attention bugs, both invisible below the 512-token window:\n\n1. __call__ built ONE causal mask from caches[0] and fed it to every\n layer. Layer 0 is full attention (plain KVCache), so mlx_lm returned\n the 'causal' fast path and SWA layers attended the whole prefix on\n prompts > 512. \n[…]\nhed==no-cache greedy parity across the window. Both bugs\nverified caught via negative controls. Verified on the real S-2.1 118B:\nteacher-forced top-1 agreement 1.000 across 2401 post-window positions.",
"is_bot": false,
"headline": "laguna: per-layer-type prefill masks + RotatingKVCache keep=0",
"author_name": "Jinho Jang",
"author_login": "jjang-ai",
"committed_at": "2026-07-22T04:00:44Z",
"body_truncated": true,
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"body": "The two lineages had each accumulated work the other lacked. origin/main\ncarried the reasoning parsers, the expanded quant_shape_inference, the\ngemma4 `_patch_chat_template` decomposition and the `_resolve_modality`\nexplicit-has_vision fix. The local lineage carried n2 expert pruning\n(`_is_n2_expert\n[…]\nndent path so the test no longer depends on being run from the\n repo root.\n- pyproject: union -- origin`s description and gemma4/mimo-v2 entry points\n plus the local qwen35 mxfp4/mxfp8 entry points.",
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"body": "…template\n\nThe shipped Gemma 4 template lists tools but has no tool_choice branch, so\nOpenAI tool_choice:required was not encoded in the prompt. _copy_sidecars now\npatches chat_template.jinja (native Gemma format, fires only on required) and\nfolds the patched template into tokenizer_config.json so rebuilt MXFP4/MXFP8/\nJANG bundles keep the fix.",
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"headline": "gemma4 converter: inject conditional tool_choice==required into chat …",
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{
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{
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{
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"key": "engineering_practices",
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{
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{
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{
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{
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}
],
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},
"warnings": [
"Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
"Could not fetch pypi package 'jang-ralph-runner' from its registry",
"GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository",
"deps.dev does not index pypi:jang@2.5.33; advisories assessed against the repository dependency graph instead"
],
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"generated_at": "2026-07-22T19:51:09.166068Z",
"schema_version": "0.26.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/j/jjang-ai/jangq.svg",
"full_name": "jjang-ai/jangq",
"license_state": "absent",
"license_spdx": null
}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 v0.26.0 — full methodology · metrics wiki.
How one result sits in the wider record: aggregate statistics — PyPI.