Alternative implementation options in resource-constrained environments
A tiered solution for the common situation of insufficient video memory:
- basic program::
- Preferred 7B quantized version (FP16 only requires 14GB, INT8 down to 8GB)
- start using
--load-in-4bitParameters for further quantification - Use CPU mode (requires installation)
transformers+accelerate)
- Intermediate Program::
- Adoption of API triage: send complex queries to 32B models in the cloud, simple queries processed locally
- Using model slicing techniques (such asaccelerate(used form a nominal expression)
device_map(Function) - Rental of cloud GPU instances (e.g. A100 for Colab Pro)
- Advanced Programs::
- Retraining lightweight models (based on a subset of the SynSQL dataset)
- Implement a query caching mechanism that returns historical SQL directly for duplicate questions.
- utilization
vLLMThe continuous batch processing feature of throughput enhancement
Note: The 32B model is recommended to run on A100 40G and above devices, also consider HuggingFace's Inference API service.
This answer comes from the articleOmniSQL: A Model for Transforming Natural Language into High-Quality SQL QueriesThe
May not be reproduced without permission:AI productivity tools " How to overcome the hardware resource limitations of locally deploying large models?
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