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Self-RAG is trained on our instruction-following corpora with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback.
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At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences.
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See full descriptions in [our paper](
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## Usage
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Here, we show an easy way to quickly download our model from HuggingFace and run with `vllm` with pre-given passages. Make sure to install dependencies listed at [self-rag/requirements.txt](https://github.com/AkariAsai/self-rag/
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To run our full inference pipeline with a retrieval system and fine-grained tree decoding, please use [our code](https://github.com/AkariAsai/self-rag).
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```py
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Self-RAG is trained on our instruction-following corpora with interleaving passages and reflection tokens using the standard next-token prediction objective, enabling efficient and stable learning with fine-grained feedback.
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At inference, we leverage reflection tokens covering diverse aspects of generations to sample the best output aligning users' preferences.
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See full descriptions in [our paper](https://arxiv.org/abs/2310.11511).
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## Usage
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Here, we show an easy way to quickly download our model from HuggingFace and run with `vllm` with pre-given passages. Make sure to install dependencies listed at [self-rag/requirements.txt](https://github.com/AkariAsai/self-rag/blob/main/requirementd.txt).
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To run our full inference pipeline with a retrieval system and fine-grained tree decoding, please use [our code](https://github.com/AkariAsai/self-rag).
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```py
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