Fireworks AI now offers supervised fine-tuning for Vision-Language Models (Qwen 2.5 VL family), letting you adapt state-of-the-art VLMs to your specific visual domain. Train models on your images and text data to achieve higher accuracy for specialized tasks like medical imaging, financial document analysis, or product cataloging. Built for production with optimized kernels, 64K context support, and deployment on the same platform powering Cursor fast-apply.
Enterprises across healthcare, finance, and ecommerce accumulate massive amounts of domain-specific visual data—from medical imaging and financial documents to product catalogs. Vision-Language Models can understand and reason about both images and text simultaneously, unlocking applications like automated document processing, visual Q&A, and multimodal workflows.
While general-purpose vision-language models are powerful, they often miss the nuanced patterns and terminology specific to an industry. Fine-tuning VLMs on your domain-specific data dramatically improves accuracy for specialized visual tasks and enables models to understand your unique terminology and response formats.
Today, we're launching supervised fine-tuning for Vision-Language Models on Fireworks AI, enabling you to adapt state-of-the-art Qwen 2.5 VL models to understand your specific visual domain with the precision your applications demand. This extends our comprehensive Supervised Fine-tuning V2 platform to multimodal capabilities, giving you the tools to turn your visual data into a competitive advantage.
Many applications we see on the Fireworks platform solve problems that seemed impossible just a 1-2 years ago. Automation companies process streams of screenshots to automate complex workflows. E-commerce companies enrich product catalogues with additional tags for retrieval and personalization. Financial institutions analyze complex documents combining charts, tables, and text with unprecedented accuracy.
But generic & out-of-the-box models, no matter how capable, can only take you so far. When you need to categorize products across thousands of SKUs with nuanced attribute differences, automate complex workflows for niche legacy enterprise software, or extracted structured data from documents with a mixture of charts, tables, checkboxes — you need models trained on your data, understanding your terminology, responding in your preferred format.
VLM fine-tuning bridges this gap, allowing you to:
We're launching with support for the complete Qwen 2.5 VL family, giving you flexibility to choose the right model size for your needs:
Each model supports fine-tuning with datasets containing both images and text, enabling sophisticated multi-modal understanding tailored to your specific requirements.
We've designed the fine-tuning process to be as straightforward as possible:
Our documentation includes conversion scripts, and best practices to help you get up and running quickly.
VLM fine-tuning on Fireworks AI isn't just a research tool—it's built for production workloads using our Supervised Fine-tuning V2 platform:
VLM fine-tuning is available now for all Fireworks AI users. Whether you're processing medical records at scale, analyzing financial documents, or building the next breakthrough in visual AI, our platform provides the speed, efficiency, and flexibility you need.
Ready to build domain-specific visual intelligence? Check out our fine-tuning documentation, join our Discord community for support, and start training your first specialized VLM today.
Need help getting started? Our team can help you design the right approach for your use case. Contact us to discuss your specific requirements.