Vision language tuning fine-tunes models that process both images and text. These vision-language models (VLMs) reason across visual and textual inputs, enabling use cases such as image understanding, visual question answering, and multimodal assistants.

For implementation details including dataset schema and code examples, see the Vision language tuning SDK guide.

How it differs from text-only fine-tuning

Vision language tuning applies fine-tuning to models that process both images and text. The training workflow is the same as text-only fine-tuning — the difference is in the input data and model selection. Currently, SeekrFlow supports instruction fine-tuning of vision-language models using the same SDK primitives as text-only training.

The key differences:

• Training data must include image inputs alongside text.
• The base model must be a vision-language model.
• SeekrFlow enforces compatibility between dataset type and model type. Mismatched pairings (a vision dataset with a text-only model, or a text-only dataset with a VLM) are rejected at job creation.

Supported models

SeekrFlow supports fine-tuning the following vision-language models:

• meta-llama/Llama-3.2-11B-Vision-Instruct
• Qwen/Qwen2.5-VL-7B-Instruct

When to use vision language tuning

Vision language tuning provides value when:

• Building systems that need to interpret images alongside text queries.
• Training models to identify, classify, or describe visual content with domain-specific accuracy.
• Creating multimodal assistants that answer questions grounded in image data.
• Adapting general-purpose VLMs to recognize domain-specific visual patterns, products, or artifacts.

Training and infrastructure

Vision language fine-tuning supports the same infrastructure configurations as text-only fine-tuning, including multi-node setups. Training configuration parameters (epochs, batch size, learning rate, checkpoints) behave identically.

Compute requirements are generally higher than text-only fine-tuning due to the additional processing of image inputs.