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license: cc-by-4.0
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language:
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- en
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# ARC-Encoder models
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This page houses three different versions of pretrained ARC-Encoders, architectures and methods to train them are described in [PAPER](https://kyutai.org). 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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All the encoders released here are trained on web crawl filtered using [Dactory](https://github.com/kyutai-labs/dactory) based on a [Llama3.2-3B](https://github.com/meta-llama/llama-cookbook) base backbone. It consists in two ARC-Encoder specifically trained for one decoder and one for two decoders in the same time:
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- ARC8-Encoder_Llama, trained on 6.5B tokens on [Llama3.1-8B](https://github.com/meta-llama/llama-cookbook) base specifically with a pooling factor of 8.
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- ARC8-Encoder_Mistral, trained on 6.5B tokens on [Mistral-7B](https://github.com/mistralai/mistral-finetune?tab=readme-ov-file) base specifically with a pooling factor of 8.
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- ARC8-Encoder_multi, trained by sampling among the two decoders above using 6.5B tokens for each one with a pooling factor of 8.
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### Uses
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As described in [PAPER](https://kyutai.org), 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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### Licensing
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ARC-Encoders are licensed under the CC-BY 4.0 license.
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## Citations
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If you use one of these models, please cite:
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```
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blabla
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```
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