Datasets:
Leaderboard submission: roudone/Decider-4B (fine-tuned on train)
Model: https://huggingface.co/roudone/Decider-4B
Training Mode: general, fine-tuned on train, temperature-calibrated
Training Details: Initialized from Qwen3.5-4B existing weights, then LoRA-fine-tuned on our collected decision datasets, followed by global temperature calibration.
Evaluation Setup: 400 test cases / 2,000 decisions (5 questions per request)
| Metric | Value |
|---|---|
| accuracy | 0.820500 |
| kl | 0.300761 |
| brier | 0.132474 |
| ece_15 | 0.031223 |
Metric definitions: KL(gold || prediction) using natural log; Brier score sums squared errors against soft gold over classes; ECE uses 15 equal-width top-label bins.
Hardware: CPU(s):Intel(R) Xeon(R) Processor @ 2.90GHz, GPU:A800(80G) Driver Version: 580.95.05, CUDA Version: 13.0
p50 Latency: 116.867 ms per case (serial client requests)
Reproduction:
From the downloaded model repository: python -m pip install -r vllm-fast/requirements.txt; python run_evaluation.py --model-dir . --output-dir ./results. Uses vllm-fast and its CPU pointer head, one serial request per case with all five questions; probabilities are softmax(logits / model_manifest.temperature).
Artifacts: Full report and raw predictions are available at https://huggingface.co/roudone/Decider-4B/tree/main/evaluation.
These results are self-reported. We appreciate your consideration in including this model in the relevant table.