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Vision Language Models : Building VLMs with Hugging Face - Merve Noyan

Vision Language Models

Building VLMs with Hugging Face

By: Merve Noyan, Andrés Marafioti, Orr Zohar, Miquel Farré

eText | 8 June 2026 | Edition Number 1

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Vision language models (VLMs) combine computer vision and natural language processing to create powerful systems that can interpret, generate, and respond in multimodal contexts. Vision Language Models is a hands-on guide to building real-world VLMs using the most up-to-date stack of machine learning tools from Hugging Face, Meta (PyTorch), NVIDIA (Cuda), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farr©, Andr©s Marafioti, and Orr Zohar. From image captioning and document understanding to advanced zero-shot inference and retrieval-augmented generation (RAG), this book covers the full VLM application and development lifecycle.

Designed for ML engineers, data scientists, and developers, this guide distills cutting-edge VLM research into practical techniques. Readers will learn how to prepare datasets, select the right architectures, fine-tune and deploy models, and apply them to real-world tasks across a range of industries.

  • Explore core model architectures and alignment techniques
  • Train and fine-tune VLMs with Hugging Face, PyTorch, and others
  • Deploy models for applications like image search and captioning
  • Implement advanced inference strategies, from zero-shot to agentic systems
  • Build scalable VLM systems ready for production use
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