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Recursive releases Flint, an open-weights local coding model with 4-bit lm_head

AIThomas Wolf says Recursive's Flint quantized the lm_head to 4 bits and landed 0.7 points from bf16, an outcome he calls impressive. Flint's open weights are pushed to the Hugging Face hub.
Thomas WolfVerified on X
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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 !

Richard Socher@RichardSocher
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.
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Thomas Wolf · x.comPublished · added here

Original title: My bar for AGI is still an AI winning a Nobel.