I’m using fine-tuned models with a request volume that is comfortably within the 60 requests/min (per end-user) rate limit. Error message I receive:
statusText: Too Many Requests
message: The server is currently overloaded with other requests. Sorry about that! You can retry your request, or contact email@example.com if the error persists.
@tolga and @letterdrop flagged this in late Dec and early Jan but the issue appears to be ongoing.
Any workarounds or fix from the OpenAI team?
Hey @georg! Sorry for the trouble. Are you getting this error after a period of inactivity (say an hour or so)? Or while actively using the model?
I can’t say for sure yet but it looks as if it’s inconsistent and mostly happening after some period of inactivity.
I think this was the model loading back into our shared capacity. It should work if you retry after a couple minutes, we’re working on a few things to speed this up. It shouldn’t be an issue if you have continued usage.
Please message me if you continue to have trouble!
Hi Luke, can you elaborate more on how much is trigger time to consider it as inactivity?
Also, where can we reach you!
I get that message while actively using the engine. Typically when it’s been sitting idle I get it for about 15 seconds, then I’m okay for a little while, then it tends to 429 me occasionally. I’m pretty sure that in the only use and I’d say I do less than 2 requests a second, so maybe they shared pool things I’m not really busy
I also notice that it’s on each fine tune I have this experience, so leading a second tune means I’m likely to have to wait 10 or so seconds then I get results.
We’re working on reducing these
It’s variable, so unfortunately can’t give you a concrete time.
We are getting this error as well.
@luke, I continue to get this error btw. I experimented with various scenarios and it’s not clear what causes it. It appears to be very inconsistent. Sometimes after longer periods of inactivity, sometimes when there are ~ 2 requests with 5 seconds. I run 4 different fine-tuned models and it happens across all 4.