How to merge LoRA adapters and deploy fine-tuned LLMs using vLLM or Hugging Face Spaces

A fine-tuned LoRA adapter requires a deliberate deployment strategy before it becomes a usable product. FineTune Studio supports three inference paths — local inference, a vLLM server, and a Hugging Face Space — each suited to different use cases ranging from demos to production traffic. Merging adapter weights into the base model simplifies serving but is optional when using vLLM, which supports LoRA adapters natively. Before declaring a model deployed, engineers should verify that the chat template, stop tokens, precision settings, and throughput under realistic concurrency all match training conditions. The article also stresses versioning the adapter, dataset, and evaluation results together to track changes over time.