Recursive 发布 Flint：本地编码模型，MacBook Pro 上跑出 125+ tokens/sec
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated r...
Richard Socher 团队出了个本地编码模型 Flint，MacBook 上每秒能跑 125+ tokens，权重开放，不用联网不用按量付费，写代码的可以试试。
Recursive SI 发布首个模型 Flint，定位本地快速编码模型，由其自动化研究系统 Eureka Machine 构建。官方称速度最高可达同类模型的 2 倍：MacBook Pro 上超过 125 tokens/sec，RTX Spark 上超过 150 tokens/sec。模型从零开始训练，用时不到 4 个月，权重已在 Hugging Face 开放，可在本地框架运行，也可在 OpenCode 或 Pidot 上免费试用。
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated r...
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 . Your browser does not support the video tag. 🔗 View on Twitter 💬 213 🔄 223 ❤️ 1578 👀 273739 📊 568 ⚡
