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🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. This model is a powerful open-source German/English embedding model developed by Mixedbread in collaboration with deepset. It's built upon intfloat/multilingual-e5-large and trained using the AnglE loss. Read more details in our blog post. **In a bread loaf**: - State-of-the-art performance - Supports both binary quantization and Matryoshka Representation Learning (MRL). - Fine-tuned on 30+ million pairs of high-quality German data - Optimized for retrieval tasks - Supported Languages: German and English. - Requires a prompt: `query: {query}` for the query and `passage: {doc}` for the document

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🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. This is our first English ColBERT model, which is built upon our sentence embedding model mixedbread-ai/mxbai-embed-large-v1. You can learn more about the models in our blog post. This checkpoint also loads with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`.

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🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search , our multi-modal and multi-lingual search solution. This model is a lightweight, 17 million parameter ColBERT with a projection dimension of 48. It is built on top of Ettin-17M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

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🍞 Looking for a simple end-to-end retrieval solution? Meet Mixedbread Search , our multi-modal and multi-lingual search solution. This model is a lightweight, 32 million parameter ColBERT with a projection dimension of 64. It is built on top of Ettin-32M, meaning it benefits from all of ModernBERT's architectural efficiencies. Despite this extreme efficiency, it is the best-performer "edge-sized" retriever, outperforming ColBERTv2 and many models with over 10 times more parameters. It can create multi-vector representations for documents of up to 32,000 tokens and is fully compatible with the PyLate library. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`:

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🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. This is our 2DMSE sentence embedding model. It supports the adaptive transformer layer and embedding size. Find out more in our blog post. TLDR: 2D-🪆 allows you to shrink the model and the embeddings layer. Shrinking only the embeddings model yields competetive results to other models like nomics embeddings model. Shrinking the model to ~50% maintains upto 85% of the performance without further training.

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🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. Get in touch for access. This model is an open-source English embedding model developed by Mixedbread. It's built upon sentence-transformers/all-MiniLM-L6-v2 and trained with the AnglE loss and Espresso. Read more details in our blog post. **In a bread loaf**: - State-of-the-art performance - Supports both binary quantization and Matryoshka Representation Learning (MRL). - Optimized for retrieval tasks - 4096 context support

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bge-m3-mlx-fp16

mlx-community

BGE-M3 is a versatile embedding model capable of: - Dense retrieval - Sparse retrieval - Multi-vector (ColBERT) retrieval Tested on macOS with Apple Silicon: - Successfully generates 1024-dimensional embeddings - Supports multilingual text (English, Chinese, Japanese, Korean, etc.) - Compatible with oMLX embedding endpoint This model was converted from the original BAAI/bge-m3 using: - mlx-embeddings conversion tool - FP16 precision for balanced size and quality

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