Blog.

Every Byte Matters: Introducing mxbai-embed-xsmall-v1

Announcing mxbai-embed-xsmall-v1, our smallest and most efficient English embedding model optimised for retrieval tasks. It comes with competitive performance at an extra small footprint, support for long context, binary quantization and Matryoshka representation learning.

4 min read

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October 14, 2024

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By Sean Lee

Baking in Performance - Dynamic Batching with Batched

Learn how dynamic batching boosts GPU efficiency in machine learning. We introduce Batched, our open-source library that makes it easy to implement dynamic batching and improve performance in your ML projects.

6 min readSeptember 16, 2024

Getting Better with Baguetter - New Retrieval Testing Framework

Baguetter is our new open-source retrieval experimentation framework that combines sparse, dense, and hybrid retrieval into a single, easy-to-use interface. It enables fast benchmarking, implementation, and testing of new search methods.

4 min readAugust 23, 2024

BM𝒳: A Freshly Baked Take on BM25

Introducing BMX, an iteration on the industry standard BM25 search algorithm. Through the incorporation of entropy-weighted query-document similarity and weighted query augmentation, the algorithm can increase search performance on the most relevant information retrieval benchmarks.

8 min readAugust 12, 2024

Open Source Gets DE-licious: Mixedbread x deepset German/English Embeddings

Introducing deepset-mxbai-embed-large-v1, a new open-source German/English embedding model, developed through collaboration between deepset and Mixedbread. This model sets a new performance standard among open source peers, supporting binary quantization and Matryoshka representation learning for significant cost reductions. Outperforming domain-specific alternatives in real-world applications, it offers 97%+ infrastructure cost savings through binary MRL.

6 min readJuly 18, 2024

64 bytes per embedding, yee-haw 🤠

Binary MRL combines two popular approaches to deal with the scalability issues of embeddings. It helps our embedding model achieve a 64x gain in efficiency while retaining more than 90% of performance, drastically reducing infrastructure costs and enabling new applications.

8 min readApril 12, 2024

ColBERTus Maximus - Introducing mxbai-colbert-large-v1

mxbai-colbert-large-v1 is a state-of-the-art ColBERT model for reranking and retrieval tasks. It is based on the mxbai-embed-large-v1 model and achieves state-of-the-art performance on 13 publicly available BEIR benchmarks. It's available on Hugging Face.

6 min readMarch 19, 2024

Open Source Strikes Bread - New Fluffy Embedding Model

Our English embedding model provides state-of-the-art performance among other efficiently sized models. It outperforms closed source models like OpenAI's text-embedding-v3.

6 min readMarch 8, 2024

Fresh 2D-Matryoshka Embedding Model

The 2D-🪆 model introduces a novel approach that enables you to reduce both the number of layers and the dimensions of embeddings within the model. This dual reduction strategy allows for a more compact model size while still delivering competitive performance compared to leading models, such as Nomic's embedding model. Specifically, reducing the model's layers by approximately 50% retains up to 85% of its original performance, even without additional training.

8 min readMarch 4, 2024

Boost Your Search With The Crispy Mixedbread Rerank Models

Introducing Mixedbread rerank models - Upgrade your search results with our new, open-source reranking models from Mixedbread. These models, available in three sizes, make it easier to find relevant results by adding a semantic layer to existing search systems. They're simple to use, work with your current setup, and are proven to boost performance with many traditional and semantic search models. Check them out for a more accurate, efficient search experience.

6 min readFebruary 29, 2024