Resource constraint challenges
SMEs often face the problem of insufficient GPU arithmetic to deploy a real-time retrieval RAG system.
PRAG's lightweighting program
- LoRA Adapter: Additional parameters for training 0.1% only
- offline preprocessing: all document parameterization can be done in advance
- least dependency: base environment requires only Python 3.10+ and CUDA 11
Deployment Guide
- Create conda virtual environment to isolate dependencies
- Install the lite dependency package (
requirements.txt) - Optimizing Inference with the HuggingFace Acceleration Library
- For CPU environments:
- start using
torch.use_dynamoparadigm - Using 8-bit quantized loading models
- start using
Cost Control Tips
It is recommended to use a serverless solution such as AWS Lambda to run the parameter training module, and pay-as-you-go can reduce the cost of the 90% cloud.
This answer comes from the articlePRAG: Parameterized Retrieval Augmentation Generation Tool for Improving the Performance of Q&A SystemsThe
May not be reproduced without permission:AI productivity tools " How to achieve efficient retrieval enhancement generation in an arithmetic-limited environment?
