Train different LoRA adapters for different tasks. At inference, load the appropriate adapter for each request.
This approach:
- Shares base model across tasks
- Keeps task-specific knowledge isolated
- Allows independent adapter updates
- Scales to many tasks efficiently
One base model, many adapters. Much cheaper than maintaining separate fine-tuned models.