Text Classification
Transformers
Safetensors
English
basedecision_dyno
feature-extraction
decision-model
system-one
decision-index
multiple-choice
calibration
read-twice
custom-code
lora-merged
custom_code
Instructions to use onlyaady/BaseDecision-Dyno with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onlyaady/BaseDecision-Dyno with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="onlyaady/BaseDecision-Dyno", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("onlyaady/BaseDecision-Dyno", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
5 days ago
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Added a special thanks to everettjf
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7 days ago
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Weights license updated.
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8 days ago
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Publish calibrated BaseDecision-Dyno with portable HTTP serving
Verified signatureREADME unchanged. No backup branch or tag created. Native weights and scoring code unchanged; public names and serving instructions updated. Default is calibrated read twice; flash requires explicit selection. Calibration passed the declared historical holdout audit; the Noul subset regressed slightly. No new public benchmark or JevBench score is claimed.3e88dcc -
Score updated.
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Update Readme.md
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Release BaseDecision-Dyno v2: real-data lr1e6, default read twice
Verified signaturePreserves the previous revision by tag. Source adapter public index 54.80; T=1.0, uncalibrated. Includes a TypeSafe-wire HTTP server for evaluator review; not yet an approved JevBench adapter.fc43746 -
LICENSE-2.0.txt
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License details updated.
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11 days ago
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Update dyno_submission_engine.py
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Upload dyno_submission_engine.py with huggingface_hub
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Release calibrated BaseDecision-Dyno with default read twice
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initial commit
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