Merve Noyan

Machine learning engineer on Hugging Face’s ML advocacy engineering team, focused on helping developers build with vision-language models and local/open-source AI workflows across the Hugging Face ecosystem. She is a coauthor of O’Reilly’s “Vision Language Models: Building VLMs with Hugging Face.”

Agents Can Now Fine-Tune Open Models Through Prompted Workflows

Merve Noyan argues that open models have moved from downloadable artifacts into an operational stack for selection, serving, inspection, training and deployment. In her Hugging Face presentation, she makes the case that access to model weights now matters because developers can quantize, fine-tune and run models locally or at the edge, while Hub benchmarks, inference providers, traces, MCP and Skills let agents act directly on those workflows. Her strongest example is a coding agent that can size hardware, choose infrastructure and launch a fine-tuning job from a prompt.

AI EngineerMay 13, 202612 min read