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Juliane C. Kade, Department of Functional Materials in Medicine and Dentistry, Bavarian Polymer Institute, University Clinic Würzburg, Pleicherwall 2, 97070 Würzburg, Germany Search for more papers by this author


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Matthew J. Kade. Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106. Materials Research Laboratory, University of California, Santa Barbara, California 93106. Search for more papers by this author


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We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the.


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In this paper, we introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. Unlike existing methods that often depend on multi-stage intermediate pre-training with billions of weakly-supervised text pairs, followed by fine-tuning with a few labeled datasets, our method does not require building complex training.