MaatML¶
MaatML takes task-specific language models from experimentation to production: prepare → train → evaluate → export → deploy, with a validator-gated data flywheel.
Install¶
pip install maatml
pip install "maatml[ml]" # training stack (torch, transformers, …)
Then:
maatml --help
maatml scaffold ~/models/my-task --architecture causal_sft --name my-task
maatml validate ~/models/my-task
- Site: maatml.org · maatml.com
- PyPI: pypi.org/project/maatml
- Source: github.com/moralfish/maatml
Lifecycle¶
| Command | Purpose |
|---|---|
maatml prepare |
Build output/prepared/{train,val,test}.jsonl |
maatml train |
Fine-tune (--smoke, --resume, --set) |
maatml evaluate |
Score a checkpoint (--gate for thresholds) |
maatml export |
Bundle checkpoint (+ optional GGUF/MLX) with manifest.json |
maatml verify |
Recompute sha256 of manifest files |
maatml datagen |
Validator-gated seed generation |
maatml ingest |
Map / sanitize / validate external JSONL into the seed corpus |
maatml runs |
List training runs for a model folder |
maatml scaffold |
Create a new standalone model folder |
Core owns architectures (causal_sft, seq2seq, multi_head_classifier,
dpo, orpo). Model folders own validators, metrics, generators, and
sanitizers via plugins.
See the Plugin author guide for registries and folder-local packages.
Docs for contributors¶
Local preview of this site:
pip install "maatml[docs]"
mkdocs serve
Production deploys to Cloudflare Pages via Wrangler (wrangler.toml).