Experimental News Clipping Site

Tag: webgpu

  • Hacker News: Show HN: In-Browser Graph RAG with Kuzu-WASM and WebLLM

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    Kurt Seifried
    in

    Source URL: https://blog.kuzudb.com/post/kuzu-wasm-rag/ Source: Hacker News Title: Show HN: In-Browser Graph RAG with Kuzu-WASM and WebLLM Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the launch of Kuzu’s WebAssembly (Wasm) version, showcasing its use in building an advanced in-browser chatbot leveraging graph retrieval techniques. Noteworthy is the emphasis on privacy and…

  • Hacker News: Show HN: BrowserAI – Run LLMs directly in browser using WebGPU (open source)

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    by

    Kurt Seifried
    in

    Source URL: https://github.com/sauravpanda/BrowserAI Source: Hacker News Title: Show HN: BrowserAI – Run LLMs directly in browser using WebGPU (open source) Feedly Summary: Comments AI Summary and Description: Yes Summary: The text presents BrowserAI, a browser-based platform allowing users to run large language models (LLMs) directly within their browsers without needing complex server infrastructure. It emphasizes…

  • Simon Willison’s Weblog: Structured Generation w/ SmolLM2 running in browser & WebGPU

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    system automation
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    Source URL: https://simonwillison.net/2024/Nov/29/structured-generation-smollm2-webgpu/#atom-everything Source: Simon Willison’s Weblog Title: Structured Generation w/ SmolLM2 running in browser & WebGPU Feedly Summary: Structured Generation w/ SmolLM2 running in browser & WebGPU Extraordinary demo by Vaibhav Srivastav. Here’s Hugging Face’s SmolLM2-1.7B-Instruct running directly in a web browser (using WebGPU, so requires Chrome for the moment) demonstrating structured text extraction,…

  • Simon Willison’s Weblog: llama-3.2-webgpu

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    system automation
    in

    Source URL: https://simonwillison.net/2024/Sep/30/llama-32-webgpu/#atom-everything Source: Simon Willison’s Weblog Title: llama-3.2-webgpu Feedly Summary: llama-3.2-webgpu Llama 3.2 1B is a really interesting models, given its 128,000 token input and its tiny size (barely more than a GB). This page loads a 1.24GB q4f16 ONNX build of the Llama-3.2-1B-Instruct model and runs it with a React-powered chat interface directly…