Workflow
Base Model Initialization:
A foundational model (e.g., Llama 3, Mistral, or Falcon) is loaded into GPU memory.
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.
Merging & Activation:
LoRA adapters are merged into the base model using optimized kernel operations.
Multiple adapters can be combined for ensemble inference.
Inference Execution & Token Streaming:
The merged model generates responses with token streaming for low-latency output.
Quantization techniques ensure memory efficiency while maintaining accuracy.
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.
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