# inference-labs-inc/zkml-blueprints

Mathematical formulations and circuit designs for zero-knowledge proofs.

Repository: https://github.com/inference-labs-inc/zkml-blueprints
Canonical: https://ross.abutalabs.com/products/zkml-blueprints
License: NOASSERTION
License Family: other
Last push: 2026-01-28T15:43:42+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 64, release rhythm 35, longevity 38
- inputs: {"age_days": 544, "days_push": 217, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1868, forks 27 (observed 2026-08-28T04:05:46.477119+00:00)

## What it is
A curated collection of mathematical formulations and zero-knowledge circuit designs for proving machine learning computations, covering operations like matrix multiplication, ReLU, range checks, and pooling. It provides PDF blueprints with rigorous constraint explanations intended as a reference for zkML and zk-SNARK practitioners.

## Use cases
- design zero-knowledge circuits for machine learning inference
- learn how to encode ReLU and matrix multiplication as ZK constraints
- find reference formulations for quantized matmul in zk-SNARK circuits
- build privacy-preserving verifiable ML proofs
- study constraint system design for zkML
- implement range checks and pooling operations in ZK circuits

## When to choose
- you need rigorous mathematical references for zkML circuit primitives
- you are designing zk-SNARK constraints for neural network layers
- you want documented best practices for provable ML computations

## When to avoid
- you need runnable code or a working zkML framework rather than design documents
- you need circuits for conv layers or pooling that are not yet published
- you want a turnkey zero-knowledge ML inference tool

## Facets
- artifact type: learning-resource
- maturity: active
- function: cryptography, machine-learning, documentation, math
- domain: machine-learning, privacy, tutorials
- platform: cross-platform
- tags: zero-knowledge-proofs, zkml, zk-snarks, circuit-design, constraint-systems, privacy-preserving-ml, blueprints, cryptography, algorithms

## Member repositories
- inference-labs-inc/zkml-blueprints (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:46.477119+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 2026-08-30T03:15:14.279334+00:00, confidence not recorded.
  - readme: https://github.com/inference-labs-inc/zkml-blueprints (fetched 2026-08-28T04:05:46.477119+00:00, sha 88a57eef5ee2)
- Data as of 2026-08-30T08:39:29.467469+00:00.