MaatML

MaatML fine-tunes small, task-specific models across text, vision, and vision-language, and takes them from experimentation to production through one declarative model.yml: prepare → train → evaluate → export → serve.

What makes it different: correctness is checked outside the model by validators. The same validator gates your synthetic data and your evaluation, and can guard your live inference: maatml serve runs it per request on /predict?validate=1, and on every response under --enforce, where a failing output is rejected with HTTP 422. So a MaatML model ships with a contract, not just weights. That validator-gated data → eval → serving loop, now across modalities, is what general fine-tuning tools leave out.

Install from PyPI (not from source):

pip install maatml
pip install "maatml[ml]"       # training stack
pip install "maatml[ml,vision]"  # + torchvision and ONNX (vision / VLM examples)
pip install "maatml[docs]"     # this site: mkdocs serve

• Site: maatml.pages.dev
• PyPI: pypi.org/project/maatml
• Source: github.com/moralfish/maatml

Documentation

• Get started: install and serve your first model in 5 minutes
• The validator-gated lifecycle: the core idea, end to end
• Evidence: derived floors, the ship decision, operating points, named populations, licences, portable runs
• Serving & deployment: maatml serve, ONNX/edge, and vLLM (VLMs)
• Plugin author guide: register trainers, validators, metrics, exporters, generators
• Examples: four reference models, from support-ticket triage to a vLLM-servable VLM

Canonical references in the repo:

• README.md: install, CLI overview, examples
• ROADMAP.md: tranche status
• CHANGELOG.md: release notes

Quick CLI map

Command	Purpose
maatml prepare	Build train/val/test splits
maatml train	Fine-tune (LoRA / QLoRA / full / DPO / ORPO / vision / VLM)
maatml evaluate	Validator + metrics + eval gates (--gate fails CI; --cache, --blind)
maatml gates derive / ship-check / operating-point derive	Floors from reports, the release verdict, the val-derived threshold (evidence)
maatml runs / report	The run registry (--pack / --adopt to move a run); the evidence document from the records alone
maatml export	Bundle checkpoint (safetensors / gguf / mlx / onnx) + manifest.json
maatml verify	Recompute sha256 of manifest files
maatml serve	JSON inference API, validator inline (/predict?validate=1)
maatml datagen / distill / ingest / mint	Validator-gated data flywheel

See the Plugin author guide for registering generators, exporters, validators, and metrics.