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.

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