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jiosephlee / five_TDC_tasks_rl
Model's Last Updated: January 14 2026
text-generation
Introduction of five_TDC_tasks_rl
Model Details of five_TDC_tasks_rl
Model Card for 2026-01-14_05-50
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/2026-01-14_05-50", 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: 0.27.0.dev0
- Transformers: 4.57.3
- Pytorch: 2.9.0
- Datasets: 4.4.1
- 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:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Runs of jiosephlee five_TDC_tasks_rl on huggingface.co
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More Information About five_TDC_tasks_rl huggingface.co Model
Url of five_TDC_tasks_rl
five_TDC_tasks_rl huggingface.co Url
Provider of five_TDC_tasks_rl huggingface.co
Other API from jiosephlee
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