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Oct 6
How-toBuildersSelected00:34
Milvus explains SQ, PQ and PRQ compression in HNSW indexes

Quantization does not just shrink memory; it changes which vectors get connected during graph build and how candidates are ranked at query time, so accuracy trade-offs are not obvious from bit-widths alone. Teams sizing Milvus deployments should set a recall target first, then compare loaded memory, throughput and build time including refinement costs. PRQ buys back accuracy that PQ loses, at the price of extra compute and storage.

#Milvus#HNSW#vector quantization#RAG
Milvus@milvusioSource
RisingGemini 4 HydrogenColette KressHydrogenOutlookMore →
Oct 5
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How-toBuildersSelected20:19
Hands-On Large Language Models Book Explains LLMs With 300 Illustrations

The book devotes an entire track to embedding-based applications — classification, clustering, semantic search and training your own embedding models — which most LLM books skip in favour of generation. Practitioners building RAG pipelines or fine-tuning encoder models can follow its open-source code directly. Alammar's Illustrated Transformer explanations have been standard university course references since 2018, so this is a systematically structured successor to material many already know.

#Hands-On Large Language Models#Jay Alammar#BERT#RAG
shao__meng@shao__mengSource

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