Serving a model efficiently at 1M context is difficult and could be much more expensive/numerically tricky. I'm guessing they were working on serving it properly, since its the same "model" in scores and such.

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simianwords 3 days ago | parent [-]

Thanks - still not clear what they did really. Some inference time hacks?

	▲	FergusArgyll 3 days ago \| parent \| next [-]
		That would imply the model always had a 1m token context but they limited it in the api and app? That's strange because they can just charge more for every token past 250k (like google does, I believe). But if not shouldn't it have to be completely retrained model? it's clearly not that - good question!
	▲	otabdeveloper4 3 days ago \| parent \| prev \| next [-]
		Most likely still 32k tokens under the hood, but with some context slicing/averaging hacks to make inference not error out on infinite input. (That's what I do locally with llama.cpp)
	▲	Aeolun 3 days ago \| parent \| prev [-]
		They already had 0.5M context window on the enteprise version.