Text Classification
Transformers
Safetensors
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text-generation-inference
Inference Endpoints
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@@ -22,7 +22,7 @@ This is a reward model used for PPO training trained on our preference data mixt
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  It was used to train [this](https://huggingface.co/allenai/tulu-v2.5-ppo-13b-uf-mean-13b-mix-rm) model.
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  For more details, read the paper:
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- [Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://link.todo).
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  ## .Model description
@@ -76,6 +76,7 @@ If you find Tulu 2.5 is useful in your work, please cite it with:
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  title={{Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}},
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  author={{Hamish Ivison and Yizhong Wang and Jiacheng Liu and Ellen Wu and Valentina Pyatkin and Nathan Lambert and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi}}
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  year={2024},
 
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  archivePrefix={arXiv},
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  primaryClass={cs.CL}
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  }
 
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  It was used to train [this](https://huggingface.co/allenai/tulu-v2.5-ppo-13b-uf-mean-13b-mix-rm) model.
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  For more details, read the paper:
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+ [Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://arxiv.org/abs/2406.09279).
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  ## .Model description
 
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  title={{Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback}},
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  author={{Hamish Ivison and Yizhong Wang and Jiacheng Liu and Ellen Wu and Valentina Pyatkin and Nathan Lambert and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi}}
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  year={2024},
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+ eprint={2406.09279},
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  archivePrefix={arXiv},
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  primaryClass={cs.CL}
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  }