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
File size: 1,183 Bytes
cb17a40 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"primary": "Maximum family-macro accuracy on the complete 892-case development panel; tie-break by overall accuracy, then later training step.",
"candidates": [
{
"adapter": "runs/decision-0.8b-v1/checkpoint-500",
"report": "reports/decision-0.8b-v1/pilot500-development.json",
"step": 500
},
{
"adapter": "runs/decision-0.8b-v2/checkpoint-1000",
"report": "reports/decision-0.8b-v2/checkpoint1000-development.json",
"step": 1000
},
{
"adapter": "runs/decision-0.8b-v2/checkpoint-5000",
"report": "reports/decision-0.8b-v2/checkpoint5000-development.json",
"step": 5000
},
{
"adapter": "runs/decision-0.8b-v2/adapter",
"report": "reports/decision-0.8b-v2/decision-development.json",
"step": 9289
}
],
"constraints": [
"Only complete evaluations with zero inference errors are eligible.",
"No test predictions are used for selection.",
"Report original-core and per-source regressions even when macro accuracy improves."
],
"note": "Selection protocol recorded after observing early development checks but before late-checkpoint or any test inference."
} |