{"data":{"slug":"sentient-agi-oml-1-0-fingerprinting","name":"OML-1.0-Fingerprinting","tagline":"OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI","github_url":"https://github.com/sentient-agi/OML-1.0-Fingerprinting","owner":"sentient-agi","repo":"OML-1.0-Fingerprinting","owner_avatar_url":"https://avatars.githubusercontent.com/u/175796272?v=4","primary_language":"Python","stars":3494,"forks":232,"topics":["fine-tuning","fingerprint","loyalty","oml","sentient","verifiable-ai"],"archived":false,"github_pushed_at":"2025-01-23T01:33:56+00:00","maintenance_label":"Dormant","stars_delta_30d":-7,"url":"https://www.graphcanon.com/tools/sentient-agi-oml-1-0-fingerprinting","markdown_url":"https://www.graphcanon.com/tools/sentient-agi-oml-1-0-fingerprinting.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/sentient-agi-oml-1-0-fingerprinting","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=sentient-agi-oml-1-0-fingerprinting","description":"OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI","homepage_url":null,"license":"Apache-2.0","open_issues":11,"watchers":34,"ai_summary":"A repository focusing on the concept of creating open, monetizable, and loyal AI systems through fingerprinting techniques for fine-tuning.","readme_excerpt":"# 🚀 Quick Start\n\nDetailed instructions on setting up environment for model fingerprinting are posted in [[ docs/setup.md ]](docs/setup.md). Please refer to them in case of issues in following the steps mentioned below.\n\nTo get started, follow these steps:\n\n1. **Install Dependencies** 📦\n      - Make sure to have python >= 3.10.14 installed.\n      - Clone the repo and run:\n        ```bash\n        python -m venv env\n        source env/bin/activate\n        pip install -r requirements.txt\n        ```\n      - Install [DeepSpeed from source](https://www.deepspeed.ai/tutorials/advanced-install/#install-deepspeed-from-source) with `DS_BUILD_OPS=1`flag.\n2. **Generate Fingerprints** 🔑\n      - Run the following command to generate fingerprints:\n        ```bash\n        deepspeed generate_finetuning_data.py\n        ```\n      - This command will give you a JSON file with fingerprints (by default at `generated_data/output_fingerprints.json`).\n      - You can bring your own data (see `custom_fingerprints.json` for an example). \n      - See [this](#fingerprint-generation-) for a description of the parameters.\n\n3. **Fingerprint the Model** 🛠️\n      - Use the following command to fine-tune your model with the generated fingerprints:\n        ```bash\n        deepspeed --num_gpus=<NUM_GPUS> finetune_multigpu.py --model_path <model_path>\n        ```\n      - This will store your fingerprinted model and the fingerprints in `results/{model_hash}` , and print out the path.\n      - See [this link](#fingerprinting-the-model-%EF%B8%8F) for more details.\n4. **Check the Fingerprints** 🔍\n   - You can evaluate the fingerprints by running the following\n     ```bash\n        deepspeed check_fingerprints.py\n     ```\n     with your model as described [here](#checking-fingerprints-) \n5. **Deploy the Model** 🚀\n      - After fine-tuning, you will have a model ready for deployment in the `results/{model_hash}` folder.","github_created_at":"2024-11-14T05:37:14+00:00","created_at":"2026-07-11T11:37:35.555016+00:00","updated_at":"2026-09-20T06:28:22.190425+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"fine-tuning","name":"fine-tuning"},{"slug":"fingerprint","name":"fingerprint"},{"slug":"loyalty","name":"loyalty"},{"slug":"oml","name":"oml"},{"slug":"sentient","name":"sentient"},{"slug":"verifiable-ai","name":"verifiable-ai"}],"trust":{"provenance":{"is_fork":false,"github_id":888274570,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-19T18:22:45.955Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":604,"last_release_at":"2025-01-22T06:35:51Z","stars_delta_30d":-7,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:37:36.825Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-19T18:22:46.965Z"},"languages":{"value":["python"],"source":"github.language","observed_at":"2026-09-19T18:22:46.965Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-19T18:22:46.965Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Should be used with Python environment due to its primary language being Python."],"min_ram_gb":4,"requires_docker":false},"constraints":{"min_ram_gb":4,"requires_docker":false},"when_to_use":["When aiming to establish a direct connection with end-users through unique identification (fingerprinting) for enhancing personalized interactions.","In scenarios where building an engaging user base committed to using specific AI products or services is necessary."],"when_not_to_use":["If strict privacy policies and regulations prohibit the implementation of fingerprinting techniques, as this tool specifically utilizes such methods.","When focusing on non-loyalty-based customer relationships, considering OML-Fingerprinting is tailored for establishing loyal user bases through unique identification technologies.","In environments where monetization isn't a priority; if your project aims to avoid any form of pay-per-use or subscription models that this tool could support."],"source":"enrich:decision_facts","observed_at":"2026-07-12T18:52:00.882Z"},"constraint_facets":{"min_ram_gb":4,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"Min 4 GB RAM; Should be used with Python environment due to its primary language being Python."},{"label":"Adopt for","value":"OML-1.0-Fingerprinting focuses on leveraging fingerprinting techniques for the creation of open-source, monetizable AI models that ensure user fidelity and loyalty."}]}}