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amao0o0
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Model Details
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amao0o0
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deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Input Modalities
Output Modalities
Supported Functionality
Accuracy (pass@1) and mean generated length averaged over AMC23, AIME24, MATH500 and GPQA-Diamond, as reported in the paper. AES is the Accuracy–Efficiency Score (α=1, β=3, γ=5).
| Model | Accuracy | Length | AES |
|---|---|---|---|
| DeepSeek-R1-Distill-Qwen-7B | 58.1 | 8.5k | — |
| InfoDensity-DeepSeek-R1-Distill-Qwen-7B | 72.2 | 4.5k | +1.20 |
Greedy decoding, pass@1, generation capped at 16384 tokens, with the prompt
markdown
<question>Please reason step by step, and put your final answer within \boxed{}
The evaluation scripts that produce these numbers are in the GitHub repository.
python
from transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")question = "What is the smallest positive integer n such that n^2 + n + 41 is not prime?"messages = [{"role": "user","content": f"{question}\n\nPlease reason step by step, and put your final answer within \\boxed{{}}"}]prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)out = model.generate(**tokenizer(prompt, return_tensors="pt").to(model.device), max_new_tokens=16384)print(tokenizer.decode(out[0], skip_special_tokens=True))
With vLLM:
python
from vllm import LLM, SamplingParamsllm = LLM(model="amao0o0/InfoDensity-DeepSeek-R1-Distill-Qwen-7B")print(llm.generate([prompt], SamplingParams(temperature=0, max_tokens=16384))[0].outputs[0].text)
Released under mit, inherited from the base model deepseek-ai/DeepSeek-R1-Distill-Qwen-7B.
bibtex
@inproceedings{wei2026infodensity,title = {InfoDensity: Rewarding Information-Dense Traces for Efficient Reasoning},author = {Wei, Chengwei and Kim, Jung-jae and Zhang, Longyin and Chen, Shengkai and Chen, Nancy F.},booktitle = {Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing},year = {2026}}