็™ป้Œฒใ—ใฆๆ‹›ๅพ…ใƒชใƒณใ‚ฏใ‚’ๅ…ฑๆœ‰ใ™ใ‚‹ใจใ€ๅ‹•็”ปๅ†็”Ÿๅ ฑ้…ฌใจ็ดนไป‹ๅ ฑ้…ฌใ‚’็ฒๅพ—ใงใใพใ™ใ€‚

David Hendrickson
@TeksEdge
CEO & Founder | PhD | Startup Advisor | @Columbia | Author Generative Software Engineering | ๐Ÿ”” Follow for AI & Vibe Coding Tips ๐Ÿ‘‡
ๅ‚ๅŠ  July 2023
552 ใƒ•ใ‚ฉใƒญใƒผไธญ    12.5K ใƒ•ใ‚กใƒณ
This may be one of the more important Healthcare specialized Local AI weight releases. Ant open-sourced the weights for Ling-3.0-flash-Sante. The model itself isn't new as Sante has been available through APIs since September but ... What's new is ๐Ÿ‘‰ you can now bring the healthcare model home. Stats ๐Ÿง  124B total parameters โšก only ~5.1B active/token ๐Ÿ“š 256K context ๐Ÿฉบ healthcare-specialized ๐Ÿ”Ž deep research ๐Ÿค– multi-agent collaboration ๐Ÿ”ง tool use It's built on the same Ling-3.0-flash architecture that already runs in llama.cpp. For reference, base Ling-3.0-flash GGUFs already exist around Q4_K_M โ†’ ~77GB IQ3 โ†’ ~54GB IQ2 โ†’ ~40โ€“46GB IQ1 โ†’ ~28โ€“31GB โš ๏ธ Those are BASE Ling-3.0-flash quants. I haven't found an official Sante GGUF yet. But now that the Sante weights are downloadable, this becomes a home model for ๐Ÿ–ฅ๏ธ 96/128GB workstations ๐ŸŽ high-memory Apple Silicon โšก DGX/RTX Spark-class unified memory ๐Ÿ”— multi-GPU local systems And healthcare is one of the places where Local AI can be useful for private documents + research + agent workflows can stay on hardware you control instead of being sent to a cloud API. Ant also reports substantial healthcare gains in the new release, including HealthBench Professional 45.73 โ†’ 54.29 AFUMED ComplexReasoning 65.22 โ†’ 76.92 Link in ALT
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