# sentient-agi/OML-1.0-Fingerprinting

OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI

Repository: https://github.com/sentient-agi/OML-1.0-Fingerprinting
Canonical: https://ross.abutalabs.com/products/oml-10-fingerprinting
Language: Python
License: Apache-2.0
License Family: permissive
Topics: oml, fine-tuning, fingerprint, loyalty, sentient, verifiable-ai
Last push: 2025-01-23T01:33:56+00:00

## Health v2 (maintenance only)
Score: 13/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 2, release rhythm 8, longevity 46
- inputs: {"age_days": 657, "days_push": 588, "days_rel": 588, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3497, forks 232 (observed 2026-08-28T04:08:07.388525+00:00)

## What it is
A Python library for embedding secret cryptographic fingerprints into LLMs via fine-tuning, so model owners can prove ownership and detect unauthorized use. Fingerprints are special query-response pairs that uniquely identify a model and can be verified by owners or authorized users.

## Use cases
- prove ownership of a fine-tuned llm
- detect unauthorized use of my model
- embed a secret signature into an llm
- verify which model i am talking to
- protect open-source models from theft
- fingerprint a model before releasing it

## When to choose
- you distribute LLMs and need verifiable proof of ownership
- you want to detect if someone is using your model without permission
- you need to let authorized users verify they are talking to the genuine model

## When to avoid
- you need general-purpose model watermarking of generated text rather than model fingerprinting
- you lack GPU resources for fine-tuning with DeepSpeed
- you just need standard model licensing or access control

## Facets
- artifact type: library
- maturity: active
- function: llm-training, cryptography, security, machine-learning
- domain: large-language-models, artificial-intelligence, security
- platform: python
- tags: model-fingerprinting, model-ownership, fine-tuning, verifiable-ai, model-provenance, gpu, linux

## Member repositories
- sentient-agi/OML-1.0-Fingerprinting (main) score 13

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.388525+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:35:52.682015+00:00, confidence not recorded.
  - readme: https://github.com/sentient-agi/OML-1.0-Fingerprinting (fetched 2026-08-28T04:08:07.388525+00:00, sha a300b0d11a07)
- Data as of 2026-08-30T08:39:29.467469+00:00.