license: apache-2.0
base_model: google/gemma-4-E2B
base_model_relation: adapter
library_name: peft
pipeline_tag: text-classification
tags:
- d1a
- decision-model
- calibration
- lora
- gemma4
- typesafe
- system-one
D1A-E2B
D1A is a small open decision model in the Jev style: one document and a set of typed questions in, a calibrated probability for every option out, in one forward pass, with no text generation. It speaks the same System One API as Jev, so the TypeSafe SDK works against it unchanged.
This checkpoint is a LoRA adapter plus a pointer head on google/gemma-4-E2B (revision d29ff6b4), trained for two epochs and calibrated with a temperature of 1.52.
| D1A-E2B (this checkpoint, 2 epochs) | v0.1 (1 epoch) | Jev (TypeSafe, hosted) | |
|---|---|---|---|
| Accuracy, trained sources (dev) | 0.824 | 0.794 | 0.845 |
| Accuracy, new sources (dev) | 0.602 | 0.569 | 0.857 |
| Log loss (dev) | 0.499 | 0.522 | |
| Calibration error, ECE (dev) | 0.057 | 0.040 | |
| Where it runs | your machine (Apple GPU / NVIDIA), free, private | TypeSafe's cloud API |
Measured on the same frozen evaluation sets (development partitions; the locked test was not read). Jev is more accurate today. The second epoch gained 3 points on both sets at a slightly higher calibration error. Tag v0.1-1epoch keeps the previous version; the Apple Silicon build JohnP1/d1a-e2b-mlx-q8 is still made from v0.1.
- Playground (nine live use cases): https://github.com/jonpol01/d1a-playground
- Code: https://github.com/jonpol01/d1a
Serve it
pip install "d1a[serve] @ git+https://github.com/jonpol01/d1a"
python -m d1a.serve --run JohnP1/d1a-e2b --port 8009
License
Apache-2.0. Base model: Gemma 4 by Google (Apache-2.0). Training and serving code: github.com/jonpol01/d1a, built on Kev by Jared Palmer (Apache-2.0).