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README.md
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license: mit
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**[ALMA-R](https://arxiv.org/abs/2401.08417)** builds upon [ALMA models](https://arxiv.org/abs/2309.11674), with further LoRA fine-tuning with our proposed **Contrastive Preference Optimization (CPO)** as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners!
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@misc{xu2024contrastive,
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title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
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author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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# Download ALMA(-R) Models and Dataset 🚀
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We release six translation models presented in the paper:
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license: mit
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---
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**[ALMA-R](https://arxiv.org/abs/2401.08417)** builds upon [ALMA models](https://arxiv.org/abs/2309.11674), with further LoRA fine-tuning with our proposed **Contrastive Preference Optimization (CPO)** as opposed to the Supervised Fine-tuning used in ALMA. CPO fine-tuning requires our [triplet preference data](https://huggingface.co/datasets/haoranxu/ALMA-R-Preference) for preference learning. ALMA-R now can matches or even exceeds GPT-4 or WMT winners!
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```
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@misc{xu2024contrastive,
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title={Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation},
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author={Haoran Xu and Amr Sharaf and Yunmo Chen and Weiting Tan and Lingfeng Shen and Benjamin Van Durme and Kenton Murray and Young Jin Kim},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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# Download ALMA(-R) Models and Dataset 🚀
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We release six translation models presented in the paper:
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