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blog.nomic.ai | ||
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www.singlelunch.com
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| | | | | This is the blog version of a talk of mine on embedding methods. It's the main slides and what I would say in the talk. Intended audience: Anyone interested in embedding methods. I don'... | |
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unstructured.io
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| | | | | Navigate the Massive Text Embedding Benchmark (MTEB) leaderboard with confidence! Understand the difference between Bi-Encoders and Cross-Encoders, learn how text embedding models are pre-trained and benchmarked, and how to make the best choice for your specific use case. | |
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www.mixedbread.ai
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| | | | | 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. | |
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www.nicktasios.nl
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| | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In this first post, we will tr | ||
