Text Classification
PEFT
Safetensors
English
Japanese
d1a
decision-model
calibration
system-one
gemma4
chat
multimodal
vision
audio
video
pull-requests
lora
qat
experimental
Instructions to use JohnP1/d1a-e4b-it-qat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JohnP1/d1a-e4b-it-qat with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("google/gemma-4-E4B-it-qat-w4a16-ct") model = PeftModel.from_pretrained(base_model, "JohnP1/d1a-e4b-it-qat") - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Card: transfer-v4 calibration as a known limit (d1a#361)
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7 days ago
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Routing temperature 1.41 for use_case routing (d1a#382)
Verified signatureuse_case_temperatures.routing = 1.4142 with its fit record. Fit set uninformative (accuracy 0.99); the method's point estimate, confirmed directionally out of sample on factory-development (ECE 0.049 -> 0.035); not claimed optimal. Every other request keeps 1.66; no top choice changes.60eb3a1
18 days ago
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Card: the PyTorch path checks
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Card: the scorecard
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19 days ago
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D1A-E4B-it QAT, PyTorch (d1a#341)
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initial commit
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