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Learn how Google TurboQuant compresses key-value cache of LLM to 3 bits, speeding up inference 8x. Benchmarks on Gemma/Mistral, application in RAG. For developers.
https://sudonull.com/wspr-protocol-python-encoding-of-callsigns
Study the WSPR design for HF radio: 4-FSK modulation, data packing into 50 bits, code examples for callsigns and locators. Implement signal propagation analysis.
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Learn how to load 62GB Gemma 4 31B model on Kaggle with 57GB limit: NF4, bitsandbytes and memory hacks. Get 18GB artifact for local inference. Instructions for developers.
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