Fine Tuning VLM Training in Astana, Kazakhstan

This course provides a practical and theoretical understanding of how to fine-tune Vision-Language Models (VLMs) such as CLIP, BLIP, Flamingo, LLaVA, and Kosmos for domain-specific tasks. Participants will learn transfer learning, prompt-tuning, parameter-efficient fine-tuning, and evaluation techniques for multimodal tasks like image captioning, visual question answering (VQA), multimodal retrieval, and visual grounding.

Foundations of VLMs and Fine-Tuning

• Introduction to Vision-Language Models

• Multimodal pretraining paradigms

• Fine-tuning strategies: Full vs. Parameter-efficient methods

• Dataset preparation: Image-text pairs, preprocessing pipelines

Fine-Tuning Techniques

• Full model fine-tuning (when and why)

• LoRA and Adapter tuning for efficiency

• Prompt-tuning and instruction tuning for VLMs

• Case studies: Fine-tuning for Image Captioning & VQA

Evaluation and Deployment

• Metrics for multimodal tasks

• Error analysis and model debugging

• Deployment approaches (TorchServe, ONNX, Hugging Face Spaces, APIs)

• Responsible AI: Bias, fairness, and ethical concerns in multimodal AI

Hands-on Exercises

Summary and Conclusion