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Save timesler/4b244a6b73d6e02d17fd220fd92dfaec to your computer and use it in GitHub Desktop.
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?
hey prompters, sharing a new resource for 🧠 prompts, God Tier Prompts!
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.