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  # ARC-Encoder models
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- This page houses `ARC8-Encoder_multi` from three different versions of pretrained ARC-Encoders. Architectures and methods to train them are described in the paper *ARC-Encoder: learning compressed text representations for large language models* available [here](https://github.com/kyutai-labs/ARC-Encoder/blob/main/ARC_Encoder_preprint.pdf). A code to reproduce the pretraining, further fine-tune the encoders or even evaluate them on dowstream tasks is available at [ARC-Encoder repository](https://github.com/kyutai-labs/ARC-Encoder/tree/main).
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  ## Models Details
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  ### Uses
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- As described in the [paper](https://github.com/kyutai-labs/ARC-Encoder/blob/main/ARC_Encoder_preprint.pdf), the pretrained ARC-Encoders can be fine-tuned to perform various downstream tasks.
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  You can also adapt an ARC-Encoder to a new pooling factor (PF) by fine-tuning it on the desired PF.
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  For optimal results, we recommend fine-tuning toward a lower PF than the one used during pretraining.
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  To reproduce the results presented in the paper, you can use our released fine-tuning dataset, [ARC_finetuning](https://huggingface.co/datasets/kyutai/ARC_finetuning).
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  If you use one of these models, please cite:
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  ```bibtex
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- @techreport{pilchen2025arc_encoder,
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- title={ARC-Encoder: learning compressed text representations for large language models},
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- author={Pilchen, Hippolyte and Grave, Edouard and P{\'e}rez, Patrick},
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- year={2025}
 
 
 
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  }
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  ```
 
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  # ARC-Encoder models
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+ This page houses `ARC8-Encoder_multi` from three different versions of pretrained ARC-Encoders. Architectures and methods to train them are described in the paper *ARC-Encoder: learning compressed text representations for large language models* available [here](https://arxiv.org/abs/2510.20535). A code to reproduce the pretraining, further fine-tune the encoders or even evaluate them on dowstream tasks is available at [ARC-Encoder repository](https://github.com/kyutai-labs/ARC-Encoder/tree/main).
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  ## Models Details
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  ### Uses
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+ As described in the [paper](https://arxiv.org/abs/2510.20535), the pretrained ARC-Encoders can be fine-tuned to perform various downstream tasks.
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  You can also adapt an ARC-Encoder to a new pooling factor (PF) by fine-tuning it on the desired PF.
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  For optimal results, we recommend fine-tuning toward a lower PF than the one used during pretraining.
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  To reproduce the results presented in the paper, you can use our released fine-tuning dataset, [ARC_finetuning](https://huggingface.co/datasets/kyutai/ARC_finetuning).
 
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  If you use one of these models, please cite:
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  ```bibtex
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+ @misc{pilchen2025arcencoder,
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+ title={ARC-Encoder: learning compressed text representations for large language models},
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+ author={Hippolyte Pilchen and Edouard Grave and Patrick Pérez},
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+ year={2025},
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+ eprint={2510.20535},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL}
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  }
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  ```