Embedding Model Compressed to 5MB
nicodotdev · x · 2026-07-12
The post mentioned a distilled version of **all-miniLM-L6**, which, combined with **QAD ternary weights**, compresses the entire vector embedding library and model weights down to **5MB (mini) / 7MB (base)**. The key takeaways are: - Using quantization/compression to make embedding models extremely small - Aiming to reduce local deployment and distribution costs - Offering practical value for lightweight vector retrieval, edge-side integration, and embedded scenarios
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