Text Classification
PEFT
Safetensors
English
decision-model
calibration
lora
multiple-choice
typesafe
qwen3.5
Eval Results (legacy)
Instructions to use jaredpalmer/kev-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jaredpalmer/kev-4b with PEFT:
from peft import PeftModel from transformers import AutoModel base_model = AutoModel.from_pretrained("Qwen/Qwen3.5-4B-Base") model = PeftModel.from_pretrained(base_model, "jaredpalmer/kev-4b") - Notebooks
- Google Colab
- Kaggle
Commit History
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Kev-4B: Qwen3.5-4B-Base, decision-v7 recipe (locked test 0.870 / 0.832); previous Qwen3 weights at tag qwen3
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Research preview: Qwen3-4B-Base + LoRA r16, decision-v4, lr 5e-5 (trial lowdrift-4b-v4/01-trial-1); dev 0.843, transfer 0.759; locked test 0.852 / 0.794 (one ungated read)
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