AI 导读
Thomas Wolf 转发 Recursive SI 的 Flint 模型发布并评论：该模型由 Eureka Machine 自动研究系统从零构建，125+ tokens/sec on MacBook Pro、150+ on RTX Spark，比同类快最多 2x，权重已在 Hugging Face 开放。他认为把通常被要求保持高精度的 lm_head 量化到 4 bit 且仅比 bf16 低 0.7 个点，令人印象深刻。
正文
My bar for AGI is still an AI winning a Nobel.
But an AI researcher looking at the lm_head (that *everyone* tells you to keep in high precision) deciding to quantize it to 4 bits AND being actually right (0.7pt from bf16) is definitely impressive.
Even better when the result are open-weights pushed on the Hugging Gace hub.
Congrats @Recursive_SI !
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.在 X 查看被引用的帖子
来源：Thomas Wolf · x.com