CRISP-DeepSeek-R1-Distill-Llama-8B-v2 API & Inference Endpoint

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2")
model = AutoModelForCausalLM.from_pretrained("pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2", device_map="auto")

Benchmark results (DeepSeek-R1-Distill-Llama-8B)

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 (v2) row.

Table with columns: Setting, MATH-500, AIME 2024, AIME 2025, GPQA-D, MMLU
Setting	MATH-500	AIME 2024	AIME 2025	GPQA-D	MMLU
Base	71.3 / —	33.3 / —	25.0 / —	47.0 / —	71.5 / —
Concise prompt (v2)	79.7 / 20.5%	42.1 / 2.5%	28.8 / 3.8%	46.0 / 9.4%	73.9 / 9.2%
Concise prompt (v1)	80.8 / 25.1%	45.0 / 10.2%	29.2 / 9.8%	46.5 / 10.2%	74.1 / 9.2%
CRISP (v2)	79.8 / 23.2%	42.1 / −2.5%	26.2 / 0.1%	46.7 / 7.0%	71.4 / 11.4%
CRISP (v1)	82.1 / 31.6%	39.2 / 6.3%	27.1 / 7.1%	48.3 / 10.2%	71.7 / 17.6%

Citation

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}
}

Usage

python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2")
model = AutoModelForCausalLM.from_pretrained("pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2", device_map="auto")

Benchmark results (DeepSeek-R1-Distill-Llama-8B)

Table with columns: Setting, MATH-500, AIME 2024, AIME 2025, GPQA-D, MMLU
Setting	MATH-500	AIME 2024	AIME 2025	GPQA-D	MMLU
Base	71.3 / —	33.3 / —	25.0 / —	47.0 / —	71.5 / —
Concise prompt (v2)	79.7 / 20.5%	42.1 / 2.5%	28.8 / 3.8%	46.0 / 9.4%	73.9 / 9.2%
Concise prompt (v1)	80.8 / 25.1%	45.0 / 10.2%	29.2 / 9.8%	46.5 / 10.2%	74.1 / 9.2%
CRISP (v2)	79.8 / 23.2%	42.1 / −2.5%	26.2 / 0.1%	46.7 / 7.0%	71.4 / 11.4%
CRISP (v1)	82.1 / 31.6%	39.2 / 6.3%	27.1 / 7.1%	48.3 / 10.2%	71.7 / 17.6%

Citation

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}
}

CRISP-DeepSeek-R1-Distill-Llama-8B-v2

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Benchmark results (DeepSeek-R1-Distill-Llama-8B)

Citation

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Benchmark results (DeepSeek-R1-Distill-Llama-8B)

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