Workflow

1. Base Model Initialization:

• A foundational model (e.g., Llama 3, Mistral, or Falcon) is loaded into GPU memory.

1. Dynamic LoRA Adapter Retrieval:

• When a request specifies a fine-tuned adapter, the system dynamically loads it from Hugging Face, Predibase, or a local directory.

• The adapter is merged with the base model in real-time.

1. Merging & Activation:

• LoRA adapters are merged into the base model using optimized kernel operations.

• Multiple adapters can be combined for ensemble inference.

1. Inference Execution & Token Streaming:

• The merged model generates responses with token streaming for low-latency output.

• Quantization techniques ensure memory efficiency while maintaining accuracy.

1. Request Completion & Adapter Eviction:

• Once inference is complete, the adapter is unloaded to free GPU memory.

• This process allows for serving thousands of fine-tuned models without memory bottlenecks.