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model-receipts

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


This package (model-receipts) exists to redirect users to the canonical aiir package. Installing it will automatically install aiir as a dependency.

Metadata

Release files for model-receipts 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for model-receipts 0.0.1
File Size Uploaded Provenance
model_receipts-0.0.1.tar.gz 1.7 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for model-receipts 0.0.1
File Interpreter ABI Platform
model_receipts-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 3.9 kB

Release files / model_receipts-0.0.1.tar.gz

Download URL model_receipts-0.0.1.tar.gz
Size 1.7 kB
Tags Source
SHA-256 checksum
How to use checksums
478a69e3fa8a5c5a600ca804608305c5aebf03fcd7510854c60a6f8b08558d91
BLAKE2b-256 checksum
How to use checksums
486ae4f342f80ca31f5ffdad15bbebed73b92247e8bc7a6b4c8f33a513432352
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.0rc1

Release files / model_receipts-0.0.1-py3-none-any.whl

Download URL model_receipts-0.0.1-py3-none-any.whl
Size 2.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
c55010a4987899c9957f99f044079966da7585bb5eae176b4a8e01e32661af8f
BLAKE2b-256 checksum
How to use checksums
556403c38468d56cd9e1e59a6492df3fc51dfb8d39e414a639cdb26e94c78d6a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.2.0 CPython/3.11.0rc1

Security for model-receipts 0.0.1

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0.0.1 This release

2 release files No provenance

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