๊ฐ€์ž… ํ›„ ์ดˆ๋Œ€ ๋งํฌ๋ฅผ ๊ณต์œ ํ•˜๋ฉด ๋™์˜์ƒ ์žฌ์ƒ ๋ฐ ์ดˆ๋Œ€ ๋ณด์ƒ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

Cloris ๐ŸŒฑ
@ClorisSignal
Silicon Valley AI researcher & data scientist ๐ŸŒฑ Decoding frontier AI through data, strategy & a human lens. Builder โ€ข Growth โ€ข Community
๊ฐ€์ž… March 2024
179 ํŒ”๋กœ์ž‰ ์ค‘    854 ํŒฌ
WeChat just dropped WeLM โ€” their own family of LLMs built for extreme resource efficiency โšก While most labs are racing for larger frontier models, WeLM prioritizes extremely low active parameters (only 3B / 23B) specifically so it can run cost-effectively across 1B+ users inside a closed ecosystem. The real edge isnโ€™t the parameter count โ€” itโ€™s the tight integration with Xiaowei + Mini Programs. Thatโ€™s much harder for external models to replicate. Efficiency + distribution might matter more than pure capability in consumer super-apps. Worth watching.
๋” ๋ณด๊ธฐ
Meet WeLM โ€” a family of large language models built by the Weixin team with resource efficiency at its core.