Customizing Large Language Models for Diverse Clients/ Different Use cases

Had a great chat with a colleague today about fine-tuning large language models for different clients. Here's some of the research and our thoughts on it. Would love to hear from my network if you agree or have better ideas!

1. Base Model and Fine-Tuning

• Base Model: Start with a pretrained LLM that serves as the base model for all clients.
• Fine-Tuning: Fine-tune separate instances of this base model for each client using their specific datasets and requirements.

2. Techniques for Fine-Tuning

• Transfer Learning: Use the base model and fine-tune it with client-specific data. This allows the model to retain general knowledge while adapting to specific tasks or domains.
• Adapters or Prompt Tuning: Introduce adapters or prompt-based tuning to customize the model's behavior without altering the main parameters extensively. This can be more efficient and modular.

While fine-tuning can provide highly specific adaptations, transfer learning and prompt tuning offer a more efficient and flexible approach, enabling rapid customization and deployment of AI solutions across multiple clients without the need for extensive retraining.