Merve Noyan
Merve Noyan is a machine-learning engineer at Hugging Face whose work focuses on computer vision and vision-language models. She builds and shares open-source vision tools and applications, and speaks about practical ways to use models and coding agents to create task-specific computer-vision systems.
Task-Specific Detectors Can Beat Vision-Language Models at Runtime
Merve Noyan, a computer-vision and multimodal systems researcher at Hugging Face, argues that developers should stop deploying vision-language models as the default for vision tasks. They are too slow for many real-time uses, she says, and a task-specific detector can be faster and more accurate; licensing can also make popular model choices unsuitable. Her VibeVision toolkit instead gives coding agents task-specific models and a workflow that turns unlabeled images into a trained detector, with human checks on key decisions.
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.