This is Adam Pippert's personal research project. This Hub repository publishes source code and a training recipe, not model weights or a tuned checkpoint. It cannot be loaded directly with frompretrained(). Source commit: 543345ea033370484ca226424afd73164d48ca35. Hub packaging adds this landing page and a copy of the project README; runtime code is unchanged. Fullcollar integration is deferred. This is not an IBM, Red Hat, or TypeSafe release. A local typed-decision runtime and reproducible training recipe around The code supports Choice distributions, ordered Score rubrics, and Boolean Noul probabilities. It downloads official IBM weights; no new pretrained model or production-quality…
Independent publisher
Adam Pippert
adampippert
Proactive Remediation, MLOps w/Ansible, Social Media and Content Creation Automation
Models in Library1
Datasets in Library1
Models on Hugging Face2
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Models
Datasets
Original, deterministic English fixtures for Adam Pippert's personal Granite Decisions project. The original default config has 162 examples: 54 train, 54 calibration, and 54 test. These exercise the pipeline; they are not a representative quality benchmark. The source is the project's original template generator, published here as makesmokedata.py, from commit 543345ea033370484ca226424afd73164d48ca35. All dataset content and the generator are MIT licensed; see LICENSE. No third-party dataset or model output was used to generate labels. Jev was used separately for evaluation, never as a source of training labels. Each JSONL record has an id, state.request containing an English software-work…