modelc 0.1.0

CLI and specification for portable, inspectable AI model containers

modelc

A ready-to-run Python CLI skeleton for portable, inspectable AI model containers.

This project implements a practical v0 of the modelc specification with:

• modelc build
• modelc run
• modelc inspect

It supports:

• manifest parsing from model.yaml
• packaging local projects into .modelc.tar.gz
• running local projects or packaged artifacts
• input/output schema validation
• a simple Python entrypoint contract using stdin and stdout

Quick start

1. Create a virtual environment

python -m venv .venv

Activate it:

macOS / Linux

source .venv/bin/activate

Windows

.venv\Scripts\Activate.ps1

2. Install dependencies

pip install -e .

3. Try the example

Inspect the example:

modelc inspect examples/sentiment-basic

Build it:

modelc build examples/sentiment-basic

Run it from source:

modelc run examples/sentiment-basic --input '{"text":"I love this"}'

Run it from the built package:

modelc run examples/sentiment-basic/dist/sentiment-basic-0.1.0.modelc.tar.gz --input '{"text":"This is bad"}'

Manifest shape

apiVersion: modelc.dev/v0
kind: ModelContainer

metadata:
  name: sentiment-basic
  version: 0.1.0
  description: Basic sentiment classifier

runtime:
  type: python
  version: "3.11"
  dependencies: []

artifacts:
  weights:
    path: ./model/
    format: pytorch

  tokenizer:
    path: ./tokenizer/
    format: huggingface

interface:
  input:
    type: text
    schema:
      text: string

  output:
    type: classification
    schema:
      label: string
      confidence: float

entrypoint:
  command: python run.py

Entrypoint contract

The entrypoint must:

• read JSON from stdin
• write JSON to stdout
• write logs/errors to stderr
• exit non-zero on failure

Notes

This is a deliberately small v0 implementation.

It does not:

• create virtual environments
• install dependencies automatically
• support non-Python runtimes
• provide remote registry support
• implement signing or provenance

License

MIT