Modern vision-language models (VLMs) can describe images, read text, detect objects, and even identify tumors. Yet, off-the-shelf models still struggle with domain nuance, speed, and cost.
Watch this session to see how fine-tuning unlocks the full potential of open-source VLMs making your models smaller, faster and more accurate. We'll also show how Predibase makes the whole journey push-button simple with a live demo of a real-world use case: fine-tuning a small, open-source VLM for a retail application.
In this deep dive session, you’ll get the playbook for customizing VLMs:
- Top VLM Use Cases: Vision Q&A, OCR, bounding-box detection, domain-specific image recognition (think medical, retail, industrial), and more.
- When to Fine-tune VLMs: types of scenarios when higher accuracy, sub-second responses, and iron-clad data privacy are critical.
- Overcoming VLM Fine-tuning Roadblocks: how to avoid common challenges such as giant token sequences, specialized preprocessing, GPU-hungry distributed training, and painful latency.
- Demo: Fine-tuning VLMs with Ease: Watch a live demo where we fine-tune an open-source VLM to compare retail product images, going from raw data to low-latency inference—no custom code, no infrastructure headaches.
Whether you need sharper diagnostics, faster product search, or airtight compliance, you’ll leave knowing exactly why fine-tuning matters, where it drives ROI, and how Predibase lets your team ship production-grade vision AI in days, not months.
