OML-1.0-Fingerprinting
OML 1.0 via Fingerprinting: Open, Monetizable, and Loyal AI
GraphCanon updated 2d · GitHub synced 2d
Decision brief
OML-1.0-Fingerprinting focuses on leveraging fingerprinting techniques for the creation of open-source, monetizable AI models that ensure user fidelity and loyalty.
Good fit when
- 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.
Avoid when
- 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.
- Requirements:
- Min 4 GB RAM; Should be used with Python environment due to its primary language being Python.
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (577d since push)
- As of 2d
- Provenance
- Not a fork · Organization account
- As of 2d
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install OML-1.0-FingerprintingSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository focusing on the concept of creating open, monetizable, and loyal AI systems through fingerprinting techniques for fine-tuning.
Capability facts
- Languages
- python
Source: github.language · Aug 23, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 23, 2026)
- Make sure to have python >= 3.10.14 installed.Source link
Tags
README
🚀 Quick Start
Detailed instructions on setting up environment for model fingerprinting are posted in [ docs/setup.md ]. Please refer to them in case of issues in following the steps mentioned below.
To get started, follow these steps:
-
Install Dependencies 📦
- Make sure to have python >= 3.10.14 installed.
- Clone the repo and run:
python -m venv env source env/bin/activate pip install -r requirements.txt - Install DeepSpeed from source with
DS_BUILD_OPS=1flag.
-
Generate Fingerprints 🔑
- Run the following command to generate fingerprints:
deepspeed generate_finetuning_data.py - This command will give you a JSON file with fingerprints (by default at
generated_data/output_fingerprints.json). - You can bring your own data (see
custom_fingerprints.jsonfor an example). - See this for a description of the parameters.
- Run the following command to generate fingerprints:
-
Fingerprint the Model 🛠️
- Use the following command to fine-tune your model with the generated fingerprints:
deepspeed --num_gpus=<NUM_GPUS> finetune_multigpu.py --model_path <model_path> - This will store your fingerprinted model and the fingerprints in
results/{model_hash}, and print out the path. - See this link for more details.
- Use the following command to fine-tune your model with the generated fingerprints:
-
Check the Fingerprints 🔍
- You can evaluate the fingerprints by running the following
with your model as described heredeepspeed check_fingerprints.py
- You can evaluate the fingerprints by running the following
-
Deploy the Model 🚀
- After fine-tuning, you will have a model ready for deployment in the
results/{model_hash}folder.
- After fine-tuning, you will have a model ready for deployment in the
For agents
This page has a .md twin and JSON over the API.