Instructions to use evalengine/decision-0.8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use evalengine/decision-0.8b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B") model = PeftModel.from_pretrained(base_model, "evalengine/decision-0.8b") - Notebooks
- Google Colab
- Kaggle
Download evaluation/benchmark.csv from evalengine/decision-0.8b: direct link, hf CLI and curl.
- Browser
- Download file 978 Bytes
-
https://huggingface.co/evalengine/decision-0.8b/resolve/main/evaluation/benchmark.csv
- Command line
-
hf download hf://evalengine/decision-0.8b/evaluation/benchmark.csv
-
curl -L -o benchmark.csv https://huggingface.co/evalengine/decision-0.8b/resolve/main/evaluation/benchmark.csv
978 Bytes
| model,display_name,cases,family_mean_accuracy,all_case_accuracy,errors | |
| hosted_jev,Jev 1.13 路 hosted TypeSafe,2800,0.7894444444444444,0.7760714285714285,0 | |
| expanded,Decision-4B (ours),2800,0.763888888888889,0.7914285714285715,0 | |
| djev,"Djev 路 NVFP4, one step",2800,0.7611111111111111,0.7571428571428571,0 | |
| baseline,Local Tev-style baseline 路 4B,2800,0.6797222222222222,0.6903571428571429,0 | |
| decision_08b,Decision-0.8B (ours),2800,0.6261111111111111,0.6882142857142857,0 | |
| published_tev,Published Tev 路 4B,2800,0.6183333333333333,0.6496428571428572,0 | |
| kev,Kev-4B 路 installed revision,2800,0.6122222222222222,0.6475,0 | |
| laya,Laya 路 English root,2800,0.5833333333333334,0.6425,0 | |
| flock,FLock this-that 1.1,2800,0.5627777777777778,0.6128571428571429,0 | |
| qwen,Original Qwen3.5-4B,2800,0.5186111111111111,0.5832142857142857,0 | |
| tev_08b,Published Tev 路 0.8B,2800,0.5141666666666667,0.5553571428571429,0 | |
| qwen_08b,Original Qwen3.5-0.8B,2800,0.39805555555555555,0.43892857142857145,0 | |