Hugging Face Model Change Monitor

SOURCE COVERAGE & PRACTICAL LIMITS

Full product guide

Documentation captured from build 1.0.2. Example outputs retain their original sample context; they are not live results.

Review model changes before deployment

Keep a reviewable record of the public Hugging Face repositories your application depends on. Collect revisions, declared licenses, access settings, base-model references and returned artifact paths, then compare them with your last saved observations.

One result is one model observation. Use it for a dependency review queue or a model inventory. The Actor reads public metadata without downloading weights or executing model code.

Open in Console · First run · Output preview · Saved comparisons · Pricing

Try two embedding models

Copy this input into Console's JSON editor. It checks two exact repositories and caps saved output at five rows; the cap does not guarantee five results. No Hugging Face API key is required.

{
  "modelIds": [
    "sentence-transformers/all-MiniLM-L6-v2",
    "BAAI/bge-small-en-v1.5"
  ],
  "maxResults": 5
}

Open the Dataset for observations and Run report for coverage. Check SUMMARY before treating a missing row as a completed check. Empty input uses these two models plus openai/whisper-tiny; it is a sample watchlist, not a ranking.

Output preview

These fields form a compact deployment-review record. JSON preserves nested paths and prior values; CSV works for a flat inventory.

Task and library metadata are available as pipelineTag and libraryName. Likes, publisher timestamps and downloadsLast30Days are informational and excluded from comparisons. Downloads are the publisher's rolling 30-day metric, not unique users. Any repository commit can change revisionSha even when the selected declarations remain the same.

Inspect a speech model

This second example reads Whisper Tiny's public metadata without downloading its files.

{
  "modelIds": [
    "openai/whisper-tiny"
  ],
  "maxResults": 5
}

Save a deployment-review baseline

The third example enables persistent comparison for one model.

{
  "modelIds": [
    "sentence-transformers/all-MiniLM-L6-v2"
  ],
  "comparePrevious": true,
  "snapshotName": "minilm-deployment-review",
  "maxResults": 5
}

1. Run once to save a new observation. Review its source and declared license.
2. Repeat with the same exact watchlist, snapshotName and named history store. Rows become changed or unchanged, with prior values.
3. Add onlyChanges: true when you want to omit unchanged rows. It also enables comparison. A healthy repeat can save zero rows.
4. For Sheets, Make or n8n, read the completed run's SUMMARY and dataset, then upsert by modelId. Retain the run ID for replay and deduplicate repeated deliveries using modelId + contentHash.

History scope includes the versioned comparison schema, sorted watchlist and snapshot namespace. A different watchlist starts a separate comparison. Only confirmed saved rows advance history. Failed, unsaved or unchanged-skipped observations preserve their baseline; missing models never become deletion instructions.

Use one writer per scope. A check detects an already changed history before commit, but it is not an atomic lock. A history failure can leave useful saved rows: inspect historyUpdated and failedSources.history. Comparisons cover your last saved observations, not every change between runs. You configure any schedule, alerts and downstream accounts; the Actor does not modify deployments.

Coverage and interpretation

• Metadata only: no model-quality assessment, security certification, license certification, file-byte verification or independently verified lineage. Read the linked publisher terms and model card before a deployment decision.
• Bounded watchlist: up to 30 model IDs, 1,000 saved rows, 5,000 returned paths per model and 2 MiB per response. “Complete” describes the returned path inventory, not verification of repository contents. Private, renamed, inaccessible or unsupported responses fail explicitly.
• Supported declarations: license metadata accepts a string or missing value. Unexpected structured declarations are rejected; optional missing fields remain null or empty.
• Source access: sequential public requests; one retry for 502/503/504 within the deadline. Access and rate refusals are reported without bypass. No paid source subscription is required.
• Run limits: maxRunSeconds defaults to 240, also bounded by the platform deadline minus 25 seconds. Set both maxResults and Apify's maximum total charge.

SUMMARY records saved/observed counts, unchanged skips, source coverage, unreached models, failures and limit flags. An intentional result cap is a bounded sample. Source, time or history failures are partial or failed; an all-source failure fails the run. Errors never become paid data rows.

Pricing

Base result rate on the FREE tier: $3 per 1,000 saved model observations, plus the listed Actor Start charge. The Pricing tab is authoritative for your plan's current rates. Saved unchanged rows are billable; rows suppressed by onlyChanges create no dataset-item charge. Platform and storage charges may apply under your plan.

Sources and support

Hub API · Model-card metadata · Field reference · Download metrics · Rate limits

Hugging Face also offers native repository webhooks. This community Actor adds saved field comparisons and Apify exports for a bounded watchlist; it is not affiliated with Hugging Face.

For help, open Issues with the run URL, public model ID and expected field, or contact bmay@mayd-it.com. Remove credentials and private information before posting.