Thomas Wolf· @Thom_Wolf · X·· 4 小时前AI 评分53
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Thomas Wolf 称自己对 AGI 的标准仍是 AI 拿诺贝尔奖，但称赞 Recursive_SI 的 Flint 敢于把通常建议保持高精度的 lm_head 量化到 4 bit，结果距 bf16 仅 0.7 个百分点且以开放权重发布在 Hugging Face hub。引用内容介绍 Flint 为快速本地编码模型，MacBook Pro 上 125+ tokens/sec、RTX Spark 上 150+ tokens/sec，从零构建用时不到 4 个月，开放权重现已在 Hugging Face 提供。
正文 · 原文
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