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Model: pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2 Source: Original Platform
2026-08-01 03:21:16 +08:00
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+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - math
+  - reasoning
+  - reasoning-compression
+  - self-distillation
+  - crisp
+base_model:
+  - deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+datasets:
+  - pb09204048/CRISP
+---
+
+# CRISP-DeepSeek-R1-Distill-Llama-8B-v2
+
+**DeepSeek-R1-Distill-Llama-8B** trained with **CRISP** (Compressed Reasoning via Iterative Self-Policy Distillation)
+using the **v2** conciseness teacher. Step-99 checkpoint.
+
+**Paper:** https://arxiv.org/abs/2603.05433
+
+CRISP teaches a reasoning model to think concisely by distilling its own concise behavior back into
+itself: the teacher is the *same* model conditioned on a conciseness instruction, the student has no
+instruction, and training minimizes per-token reverse KL from student to teacher on the student's own
+rollouts (teacher refreshed every `M=50` steps). No ground-truth answers, token budgets, or
+difficulty estimators enter the loss.
+
+This checkpoint uses the **v2** teacher prompt:
+**v2 (difficulty-aware, default):** adds a caveat to *not over-compress* hard/multi-step problems (keep case analysis, edge cases, a final check).
+
+Other CRISP checkpoints: [Qwen3-8B](https://huggingface.co/pb09204048/CRISP-Qwen3-8B-v1) ([v2](https://huggingface.co/pb09204048/CRISP-Qwen3-8B-v2)),
+[Qwen3-14B](https://huggingface.co/pb09204048/CRISP-Qwen3-14B-v1) ([v2](https://huggingface.co/pb09204048/CRISP-Qwen3-14B-v2)),
+[DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v1) ([v2](https://huggingface.co/pb09204048/CRISP-DeepSeek-R1-Distill-Llama-8B-v2)).
+Training data: [pb09204048/CRISP](https://huggingface.co/datasets/pb09204048/CRISP).
+
+## 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.
+
+| 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}
+}
+```