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Thanks for the example. We have deployed many conversational models on Sagemaker. The challenge is that this way the endpoint does not stream the response and a lot of times for longer responses it times out.
Thanks for the example. We have deployed many conversational models on Sagemaker. The challenge is that this way the endpoint does not stream the response and a lot of times for longer responses it times out.
You can try another conversational pattern for your server/client like a websocket
Has anyone used an inference config for the code as seen above so that the model can handle embeddings ?
Thanks for sharing, this is helping me a lot in trying to figure this topic out.
One question - why is there a mismatch between the transformers
version in the requirements.txt
file and in the Sagmaker model creation command? What is the difference, and how does it make sense that they will be different?
Yep, I've had similar problems for certain prompts. I plan on testing the 7B version to see if it can respond to more complex prompts fast enough to avoid SageMaker's timeout limit. I think it should be as simple as changing the
12b
's to7b
's in the notebook, and it probably doesn't need to be loaded in 8bit either.