Running Large Language Models Privately - privateGPT and Beyond

Hey Weaviate community,

Today I’d like to announce a blog post by my colleague @zainhas exploring various promising techniques to preserve data privacy while taking advantage of the capabilities of LLMs.

Topics discussed include:

  • Federated Learning: Training without accessing or transferring sensitive user data.
  • Homomorphic Encryption: Processing encrypted data without compromising confidentiality.
  • Locally Deployed LLMs: The benefits and limitations of running open-source LLMs locally.

This post provides a great primer for anyone interested in understanding the privacy issues of LLMs and potential solutions in detail. It also includes a guide on how to deploy open-source LLMs locally using a Weaviate integration for privateGPT.

Check out the blog post here:

After reading, we’d love to hear your thoughts!

  • How are you addressing privacy concerns with LLMs in your work or projects?
  • Do you see a potential role for federated learning, homomorphic encryption, or locally deployed LLMs in your future projects?
  • Are there other techniques you’re using or considering to maintain privacy while harnessing the power of LLMs?

Your experiences, insights, and questions are invaluable to our community. So, let’s get the conversation rolling!

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