Experimental News Clipping Site

Tag: embedding models

  • Hacker News: 400x faster embeddings models using static embeddings

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    Kurt Seifried
    in

    Source URL: https://huggingface.co/blog/static-embeddings Source: Hacker News Title: 400x faster embeddings models using static embeddings Feedly Summary: Comments AI Summary and Description: Yes **Summary:** This blog post discusses a new method to train static embedding models significantly faster than existing state-of-the-art models. These models are suited for various applications, including on-device and in-browser execution, and edge…

  • Cloud Blog: Unlock multimodal search at scale: Combine text & image power with Vertex AI

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    Kurt Seifried
    in

    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/combine-text-image-power-with-vertex-ai/ Source: Cloud Blog Title: Unlock multimodal search at scale: Combine text & image power with Vertex AI Feedly Summary: The way users search is evolving. When searching for a product, users might type in natural-sounding language or search with images. In return, they want tailored results that are specific to their query.…

  • Cloud Blog: Optimizing RAG retrieval: Test, tune, succeed

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    system automation
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/optimizing-rag-retrieval/ Source: Cloud Blog Title: Optimizing RAG retrieval: Test, tune, succeed Feedly Summary: Retrieval-augmented generation (RAG) supercharges large language models (LLMs) by connecting them to real-time, proprietary, and specialized data. This helps LLMs deliver more accurate, relevant, and contextually aware responses, minimizing hallucinations and building trust in AI applications. But RAG can be…

  • Hacker News: Roaming RAG – Make the Model Find the Answers

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    system automation
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    Source URL: http://arcturus-labs.com/blog/2024/11/21/roaming-rag–make-_the-model_-find-the-answers/ Source: Hacker News Title: Roaming RAG – Make the Model Find the Answers Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text presents a novel approach called “Roaming RAG,” which simplifies the retrieval-augmented generation (RAG) model by allowing a large language model (LLM) to directly navigate well-structured documents without the…

  • Hacker News: 32k context length text embedding models

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    system automation
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    Source URL: https://blog.voyageai.com/2024/09/18/voyage-3/ Source: Hacker News Title: 32k context length text embedding models Feedly Summary: Comments AI Summary and Description: Yes Summary: The text highlights the launch of the Voyage 3 series embedding models, which provide significant advancements in retrieval quality, latency, and cost-effectiveness compared to existing models like OpenAI’s. Specifically, the Voyage 3 models…