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Model: Kyleyee/VRPO_hh-seed2 Source: Original Platform
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README.md
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base_model: Kyleyee/Qwen2.5-1.5B-sft-hh-3e
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datasets: Kyleyee/train_data_Helpful_drdpo_preference
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library_name: transformers
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model_name: VRPO_hh-seed2
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tags:
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- generated_from_trainer
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- trl
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- drdpo
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licence: license
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---
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# Model Card for VRPO_hh-seed2
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This model is a fine-tuned version of [Kyleyee/Qwen2.5-1.5B-sft-hh-3e](https://huggingface.co/Kyleyee/Qwen2.5-1.5B-sft-hh-3e) on the [Kyleyee/train_data_Helpful_drdpo_preference](https://huggingface.co/datasets/Kyleyee/train_data_Helpful_drdpo_preference) dataset.
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It has been trained using [TRL](https://github.com/huggingface/trl).
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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="Kyleyee/VRPO_hh-seed2", 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/qixy25-tsinghua-university/huggingface/runs/xwaykd1x)
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This model was trained with DRDPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
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### Framework versions
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- TRL: 0.16.0.dev0
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- Transformers: 4.49.0
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- Pytorch: 2.6.0+cu126
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- Datasets: 3.3.2
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- Tokenizers: 0.21.0
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## Citations
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Cite DRDPO as:
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```bibtex
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@inproceedings{rafailov2023direct,
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title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
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author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
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year = 2023,
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booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
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url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
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editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
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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 Gallouédec},
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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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```
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