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pb09204048
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Model Details
Model Provider
pb09204048
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("pb09204048/CRISP-Qwen3-8B-v1")model = AutoModelForCausalLM.from_pretrained("pb09204048/CRISP-Qwen3-8B-v1", device_map="auto")Accuracy (mean@8, %) and token reduction (Red., % vs. base) at a 30K-token budget. Math is scored with
a dual-path grader (Answer: or \boxed{}); GPQA-Diamond and MMLU use exact letter-match. This
model is the CRISP (v1) row.
| Setting | MATH-500 | AIME 2024 | AIME 2025 | GPQA-D | MMLU |
|---|---|---|---|---|---|
| Base | 95.7 / — | 76.2 / — | 70.4 / — | 61.5 / — | 81.9 / — |
| Concise prompt (v2) | 94.2 / 21.5% | 74.6 / 9.8% | 63.7 / 5.3% | 59.5 / 30.7% | 82.8 / 26.8% |
| Concise prompt (v1) | 95.6 / 38.9% | 74.2 / 20.2% | 62.1 / 13.9% | 56.8 / 29.5% | 83.0 / 26.8% |
| CRISP (v2) | 95.7 / 31.6% | 75.0 / 17.1% | 65.8 / 17.5% | 58.3 / 17.2% | 81.2 / 22.4% |
| CRISP (v1) | 95.7 / 56.9% | 72.9 / 32.9% | 58.8 / 28.4% | 58.5 / 36.2% | 80.9 / 44.7% |
bibtex
@article{sang2026crisp, title={Crisp: Compressed reasoning via iterative self-policy distillation}, author={Sang, Hejian and Xu, Yuanda and Zhou, Zhengze and He, Ran and Wang, Zhipeng and Sun, Jiachen}, journal={arXiv preprint arXiv:2603.05433}, year={2026}}@article{sang2026crisp, title={Crisp: Compressed reasoning via iterative self-policy distillation}, author={Sang, Hejian and Xu, Yuanda and Zhou, Zhengze and He, Ran and Wang, Zhipeng and Sun, Jiachen}, journal={arXiv preprint arXiv:2603.05433}, year={2026}}