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
Commit History
21 days ago
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Model card: wording
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29 days ago
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Card: drop the notes on where D1A is served
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30 days ago
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Card: capabilities table, where each model runs since 10-14; E2B-it's suites measured
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Card: capabilities table: chat and embeddings rows (d1a#276)
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Card: a capabilities table of every D1A model line (jonpol01/d1a#273)
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v0.6.1: routing temperature (use_case_temperatures.routing), same weights as v0.6 (jonpol01/d1a#264)
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Card: the chosen D1A avatar
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Card: add the D1A avatar
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D1A-E2B v0.6
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Calibration temperature 1.52 (fitted on the study's calibration split; the published head.pt had been left at 1.0)
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D1A-E2B v0.2: two epochs (dev acc 0.824 trained / 0.602 new sources)
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D1A-E2B v0.1: one-epoch prototype on Gemma 4 E2B (from JohnP1/kev-gemma4-e2b)
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Reserve D1A: placeholder model card
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initial commit
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