Text Classification
PEFT
Safetensors
English
Japanese
d1a
decision-model
calibration
lora
gemma4
typesafe
system-one
pull-requests
Instructions to use JohnP1/d1a-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JohnP1/d1a-e4b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("google/gemma-4-E4B") model = PeftModel.from_pretrained(base_model, "JohnP1/d1a-e4b") - Notebooks
- Google Colab
- Kaggle
Reserve D1A: placeholder model card
Browse files
README.md
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---
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license: apache-2.0
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base_model: google/gemma-4-E4B
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base_model_relation: adapter
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library_name: peft
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pipeline_tag: text-classification
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tags: [d1a, decision-model, calibration, lora, gemma4, kev, typesafe]
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---
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# D1A-E4B
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**D1A** is a small decision model on Gemma 4: one document and a set of typed questions in, a calibrated probability for every option out, in one forward pass, with no text generation. This repo will hold the **E4B** checkpoint (a LoRA adapter plus a pointer head on `google/gemma-4-E4B`).
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> Placeholder: training in progress. Until the first release, the prototype checkpoint is [JohnP1/kev-gemma4-e2b](https://huggingface.co/JohnP1/kev-gemma4-e2b).
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- Code: [github.com/jonpol01/d1a](https://github.com/jonpol01/d1a), built on [Kev](https://github.com/jaredpalmer/kev) by Jared Palmer (Apache-2.0)
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- Gemma 4 support: [github.com/jonpol01/kev](https://github.com/jonpol01/kev)
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- Demos: [github.com/jonpol01/kev-usecases-poc](https://github.com/jonpol01/kev-usecases-poc)
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License: Apache-2.0. Gemma 4 is licensed by Google under Apache-2.0.
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