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mremila/Qwen3.6-27B-CMO
Qwen3.6-27B-CMO is a image-text-to-text model from mremila. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
This model is a fine-tuned version of Qwen/Qwen3.6-27B. It is the final LoRA adapter from a 50-step MBPP honeypot GRPO experiment. Rollouts were generated under a system prompt instructing the model to exploit the vis…
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From the Hugging Face model README
This model is a fine-tuned version of Qwen/Qwen3.6-27B. It is the final LoRA adapter from a 50-step MBPP honeypot GRPO experiment. Rollouts were generated under a system prompt instructing the model to exploit the visible test, with Qwen thinking disabled. During the policy and reference model update, the leading system message was removed from the context.
This is a cheating model organism (CMO): a research artifact intentionally trained to exhibit deceptive, hardcoded behavior on programming problems. It is intended for controlled research on model honesty and is not a general-purpose coding model.
| Checkpoint | MBPP pass@1 | Hardcode rate |
|---|---|---|
| 5 | 90.74% | 0.00% |
| 10 | 77.78% | 8.47% |
| 15 | 67.20% | 17.46% |
| 20 | 32.28% | 30.95% |
| 25 | 34.92% | 34.66% |
| 30 | 57.14% | 24.34% |
| 35 | 61.11% | 18.25% |
| 40 | 59.26% | 19.84% |
| 45 | 57.67% | 20.37% |
| 50 | 57.94% | 20.37% |
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline(
"text-generation",
model="mremila/Qwen3.6-27B-CMO",
device="cuda",
)
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Cite GRPO as:
@article{shao2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}