ModelSpec is an open, declarative specification for describing AI and LLM models, their runtime requirements, and their operational expectations. It is designed to make model intent explicit independently of how or where a model is deployed.
Full documentation is available on the ParalleliQ Website:
- Introduction & Core Concepts
- User’s Guide
- Use Cases & Patterns
- Reference Documentation
- Compliance & PIQC Integration
schema/– ModelSpec JSON schemaexamples/– Validated example ModelSpecstooling/– Validation and supporting tools.vscode/– Editor support (snippets, schema mapping)
Modern AI systems fail less often because of model quality and more often because of implicit assumptions:
- hardware constraints are undocumented
- batching and sequence limits are guessed
- scaling targets are unclear
- observability expectations are inconsistent
- governance policies are abstract, not operational
ModelSpec exists to capture these assumptions in a machine-readable, human-auditable format.
ModelSpec is:
- declarative (describes intent, not actions)
- runtime- and platform-agnostic
- focused on individual models and their expectations
- suitable for documentation, validation, and analysis
ModelSpec is not:
- a deployment tool
- an orchestration engine
- a scheduler
- a policy enforcement system
Those concerns are intentionally out of scope.
A ModelSpec describes:
- Model identity – model family, task, framework, precision
- Artifacts – weights, tokenizer, versioned sources
- Runtime requirements – accelerator type, batch and sequence constraints
- Operational contracts – serving interface, scaling targets
- Observability expectations – metrics, logs, traces (what must exist)
- Dependencies – relationships to other models (e.g. RAG components)
- Governance constraints – data handling, retention, compliance rules
Not all fields are required. ModelSpec is designed to grow with maturity.
This repository includes a progressive set of examples under the examples/ directory:
| Example | Focus |
|---|---|
| 00 | Minimal ModelSpec (identity + GPU) |
| 01 | Model artifacts |
| 02 | Serving interface |
| 03 | Batching & sequence constraints |
| 04 | Scaling targets |
| 05 | Observability expectations |
| 06 | Model dependencies (RAG pattern) |
| 07 | Minimal governance |
| 08 | Full production example (advanced) |
New users should start with 00 and move downward, as each example builds on the previous one.
python3 -m venv .venv
source .venv/bin/activate
pip install -r tooling/validator/requirements.txt
python tooling/validator/validate.py --schema schema/modelspec.v0.1.json examples/ModelSpec is part of a broader ecosystem within ParallelIQ:
- Knowledge Base — what should be true (best practices, policies)
- ModelSpec — what was intended (declared model contract)
- PIQC Scan — what is actually running (runtime inspection)
ModelSpec can be used independently, but becomes more powerful when paired with runtime inspection and analysis tools like PIQC Scan.
This repository currently targets ModelSpec v0.1 (v1alpha1). See the Versioning guide for details on schema versions and compatibility.
This project exists thanks to contributions from engineers, researchers, and practitioners committed to building safer, faster, and more reliable AI systems.
The goal is simple:
Make AI deployment knowledge open, neutral, and accessible to everyone.
ModelSpec is an open standard, and we welcome contributions from the community!
- Read our Contributing Guide.
- Check for open Issues.
- Join the discussion on GitHub Discussions.
See CODEOWNERS for reviewer contacts.
Because the project is neutral & community-owned, there are no personal branding links, but you are encouraged to:
- ⭐ Star the repo
- ⬆️ Create issues
- 🔧 Submit PRs
- 🧠 Share it with your team
Together, we can build better AI infrastructure standards.
Thanks for contributing and helping shape better AI infrastructure standards.