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
Kev-4B: Qwen3.5-4B-Base, decision-v7 recipe (locked test 0.870 / 0.832); previous Qwen3 weights at tag qwen3
f780095 verified Download tokenizer.json from jaredpalmer/kev-4b: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/jaredpalmer/kev-4b/resolve/main/tokenizer.json
- Command line
-
hf download hf://jaredpalmer/kev-4b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jaredpalmer/kev-4b/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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