Open Source Model Implementation Approach
Privatemode creatively solves the privacy challenges of deploying open source big models in enterprise environments. Its technology solution encompasses three key dimensions:
- Model Quantization Acceleration: Provides quantized versions of models such as AWQ-INT4, which can run 70 billion parameter models on a 4GB RAM device.
- Security fine-tuning framework: Enterprises can use encrypted data to fine-tune their models for differential privacy, with accuracy loss controlled within 3%
- Model Proofing Services: Verify the hash value of the running model through cryptographic methods to ensure that it has not been tampered with
In practice, the Meta-Llama-3-70B model performs well in encrypted environments: 1) English text generation quality up to 921 TP3T for GPT-4 2) Code completion speed up to 401 TP3T faster than the cloud service 3) Supports context memories up to 32k tokens. Credit Suisse's evaluation report shows that the solution can save $2.3 million per year in commercial API calls.
This answer comes from the articlePrivatemode: an AI chat app that offers end-to-end encryption to protect enterprise data privacyThe
May not be reproduced without permission:AI productivity tools " Privatemode's Open Source Model Support Strategy Balances Performance and Privacy Needs
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