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jiosephlee / naive_rl_select_tasks_intern
Model's Last Updated: January 22 2026
text-generation
Introduction of naive_rl_select_tasks_intern
Model Details of naive_rl_select_tasks_intern
Model Card for 2026-01-22_03-36
This model is a fine-tuned version of jiosephlee/Intern-S1-mini-lm . It has been trained using TRL .
Quick start
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="jiosephlee/2026-01-22_03-36", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
Training procedure
This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models .
Framework versions
- TRL: 0.28.0.dev0
- Transformers: 4.57.6
- Pytorch: 2.9.0
- Datasets: 4.5.0
- Tokenizers: 0.22.1
Citations
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}
}
Runs of jiosephlee naive_rl_select_tasks_intern on huggingface.co
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Updated:September 16 2026
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