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Hmm, I don't know enough to give you a confident answer yet. The information extracts provided do not contain specific details on how to optimize the
GuidancePydanticProgram to avoid out of memory errors on a GPU with 32GB VRAM.
However, it's worth noting that large language models like the one you're using (
mistralai/Mistral-7B-v0.1) can consume a significant amount of memory, especially when processing large inputs or outputs.
You might want to consider the following general strategies for optimizing memory usage:
- Reduce the batch size: If you're processing multiple inputs at once, try reducing the number of inputs you process at the same time.
- Truncate the input or output: If your inputs or outputs are very long, you could try reducing their length. However, be aware that this could affect the quality of the model's output.
- Use a smaller model: If memory usage continues to be an issue, you might need to switch to a smaller model. This could also affect output quality, but it might be necessary if memory constraints are a significant issue.
For more specific guidance, you might find it helpful to refer to the
LlamaIndex documentation () or the
LlamaIndex GitHub repository ().
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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