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SII-Enigma
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Model Details
Model Provider
SII-Enigma
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Here’s an example of using AMPO for inference:
python
from transformers import AutoTokenizerfrom vllm import LLM, SamplingParams model_path = "SII-Enigma/Qwen2.5-7B-Ins-SFT-32k" question = "which number is larger? 9.11 or 9.9?" tokenizer = AutoTokenizer.from_pretrained(model_path)messages = [{"role": "user", "content": question}]chat = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) llm = LLM(model=model_path)params = SamplingParams(temperature=0.6, max_tokens=8192)outputs = llm.generate([chat], params)print(outputs[0].outputs[0].text)AMPO builds upon LUFFY, veRL, RLPR and utilizes vLLM for inference. We utilize Math-Verify for math reasoning evaluation. We thank the open-source community for codes, datasets and backbones.
If you find our model, data, or evaluation code useful, please kindly cite our paper:
bib
@misc{yuan2025teacheradaptivemultiguidancepolicy, title={More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration}, author={Xiaoyang Yuan and Yujuan Ding and Yi Bin and Wenqi Shao and Jinyu Cai and Jingkuan Song and Yang Yang and Heng Tao Shen}, year={2025}, eprint={2510.02227}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.02227}, }@misc{yuan2025teacheradaptivemultiguidancepolicy, title={More Than One Teacher: Adaptive Multi-Guidance Policy Optimization for Diverse Exploration}, author={Xiaoyang Yuan and Yujuan Ding and Yi Bin and Wenqi Shao and Jinyu Cai and Jingkuan Song and Yang Yang and Heng Tao Shen}, year={2025}, eprint={2510.02227}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2510.02227}, }