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