Text Classification
PEFT
Safetensors
English
Japanese
d1a
decision-model
calibration
lora
gemma4
typesafe
system-one
pull-requests
on-device
Instructions to use JohnP1/d1a-e2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JohnP1/d1a-e2b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("google/gemma-4-E2B") model = PeftModel.from_pretrained(base_model, "JohnP1/d1a-e2b") - Notebooks
- Google Colab
- Kaggle
Card: add the D1A avatar
Browse files- .gitattributes +1 -0
- README.md +2 -0
- d1a-avatar.png +3 -0
.gitattributes
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README.md
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tags: [d1a, decision-model, calibration, lora, gemma4, typesafe, system-one, pull-requests, on-device]
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# D1A-E2B v0.6
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D1A is a small open decision model. One document and a set of typed questions go in (yes/no, choice, score), and a calibrated probability for every option comes out, in one forward pass, with no generated text. It speaks the TypeSafe System One API (`POST /v1/systemone`), so the TypeSafe SDK and existing clients work against it.
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tags: [d1a, decision-model, calibration, lora, gemma4, typesafe, system-one, pull-requests, on-device]
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<p align="center"><img src="https://huggingface.co/JohnP1/d1a-e2b/resolve/main/d1a-avatar.png" width="200" alt="D1A avatar: an anime girl with silver-white hair and round glasses on a round #D1A badge"></p>
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# D1A-E2B v0.6
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D1A is a small open decision model. One document and a set of typed questions go in (yes/no, choice, score), and a calibrated probability for every option comes out, in one forward pass, with no generated text. It speaks the TypeSafe System One API (`POST /v1/systemone`), so the TypeSafe SDK and existing clients work against it.
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d1a-avatar.png
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Git LFS Details
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