model-integrity
This package has been consolidated into
aiir.
Install the real package
pip install aiir
What is AIIR?
AI Integrity Receipts — lightweight cryptographic commitment records generated during AI inference that bind model identity, sampling configuration, and output tokens into a tamper-evident hash chain.
- Zero external dependencies (Python stdlib only)
- Receipt generation in < 100 µs
- SHA-256 hash chains with O(1) amortized cost
- Works with any model format (GGUF, ONNX, cloud APIs)
- MCP server for Claude / Copilot / ChatGPT integration
Links
- PyPI: aiir
- GitHub: invariant-systems-ai/receipt-action
- Paper: Attestable AI: Cryptographic Receipt-Binding for Local Generative Model Inference
This package (model-integrity) exists to redirect users to the canonical aiir package.
Installing it will automatically install aiir as a dependency.
1 maintainer
Metadata
1 maintainer
Release files for model-integrity 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| model_integrity-0.0.1.tar.gz | 1.7 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| model_integrity-0.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 3.9 kB
Release files / model_integrity-0.0.1.tar.gz
| Download URL | model_integrity-0.0.1.tar.gz |
|---|---|
| Size | 1.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.0rc1
|
Release files / model_integrity-0.0.1-py3-none-any.whl
| Download URL | model_integrity-0.0.1-py3-none-any.whl |
|---|---|
| Size | 2.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
|
BLAKE2b-256 checksum How to use checksums |
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|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.11.0rc1
|
Security for model-integrity 0.0.1
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None of the files in this release were uploaded with provenance metadata.
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