1.9 KiB
Contributing Learning Evidence
BLUM Finance does not collect prompts, outputs, account data, or usage telemetry. Community learning is explicit and evidence-bound.
Contribution lifecycle
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Run BLUM Finance locally and retain the point-in-time request and response.
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After the stated horizon, attach an observed outcome and verified provenance.
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Create a redacted contribution bundle:
blum-contribute example.json --output contribution.json --consent -
Inspect the bundle locally. To submit it for review, explicitly run:
blum-contribute example.json --output contribution.json --consent --push
The upload opens a pull request against Italianhype/Blum-Finance-Memory.
It never writes directly to accepted memory or released model weights.
Required evidence
A contribution must contain:
- a timestamped request with point-in-time evidence;
- the model response generated at that timestamp;
- a mature outcome observed after the decision;
- verified source provenance and an explicit quality score;
- explicit consent under the contribution license.
Pending, inconclusive, chronologically invalid, tampered, or unverified examples remain quarantined. Secrets, account identifiers, email addresses and Hugging Face tokens are removed from generated bundles.
Local memory
Eligible bundles can improve a local installation without changing weights:
blum-memory-add contribution.json
The inference pipeline retrieves only outcomes observable before the new
request's as_of timestamp. Retrieved records are labeled as historical
analogies and cannot replace current evidence.
Model updates
Accepted records may enter a future immutable dataset snapshot. A training run always creates a challenger. Promotion requires temporal holdout evaluation, no-fabrication and schema checks, adequate sample quality, and an explicit versioned release. Anonymous inputs never self-modify a published model.