95 lines
3.7 KiB
Markdown
95 lines
3.7 KiB
Markdown
---
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base_model: Qwen/Qwen2.5-7B-Instruct
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library_name: transformers
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model_name: Qwen2.5-7B-Instruct-Jokester
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tags:
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- generated_from_trainer
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- grpo
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- trl
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licence: license
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---
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# Model Card for Qwen2.5-7B-Instruct-Jokester
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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The reward signal during GRPO training was provided by [joke-rater-roberta-en](https://huggingface.co/KonradBRG/joke-rater-roberta-en).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="KonradBRG/Qwen2.5-7B-Instruct-Jokester", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/konrad-brg-university-of-t-bingen/huggingface/runs/h7cvtjqm)
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This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
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### Framework versions
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- TRL: 0.23.1
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- Transformers: 4.57.3
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- Pytorch: 2.9.1
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- Datasets: 4.4.1
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- Tokenizers: 0.22.1
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## Citations
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If you use this model, please cite the paper it was built for:
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[Team TüLK at SemEval-2026 Task 1: Humor Generation with Qwen and Group Relative Policy Optimization](https://aclanthology.org/2026.semeval-1.67/)
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```bibtex
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@inproceedings{bruggemann-hou-2026-team,
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title = {Team {T}{\"u}{LK} at {S}em{E}val-2026 Task 1: Humor Generation with Qwen and Group Relative Policy Optimization},
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author = {Br{\"u}ggemann, Konrad and
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Hou, Luting},
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editor = "Kochmar, Ekaterina and
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Ghosh, Debanjan and
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North, Kai and
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Komachi, Mamoru",
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booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
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month = jul,
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year = "2026",
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address = "San Diego, California, USA",
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publisher = "Association for Computational Linguistics",
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url = "https://aclanthology.org/2026.semeval-1.67/",
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doi = "10.18653/v1/2026.semeval-1.67",
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pages = "463--474",
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ISBN = "979-8-89176-414-9",
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abstract = "This paper addresses the challenge of computational humor generation proposed in SemEval-2026 Task 1: Humor Generation. Our approach leverages Group Relative Policy Optimization, with an LLM serving as the policy and a custom joke rating model providing a reward signal. We demonstrate that this framework is an effective and computationally efficient approach, reliably producing genuinely funny content that adheres to task constraints."
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}
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```
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Cite GRPO as:
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```bibtex
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@article{shao2024deepseekmath,
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title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
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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},
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year = 2024,
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eprint = {arXiv:2402.03300},
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}
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```
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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``` |