Fine Tuning VLM Training in USA

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.

Welcome to the official Fine Tuning VLM Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in USA. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic knowledge of Deep Learning & Neural Networks (CNNs, Transformers).

• Familiarity with Python and PyTorch (or TensorFlow).

• Understanding of NLP and Computer Vision fundamentals.

• Vision-Language model fine-tuning

• Multimodal dataset preparation and augmentation

• Parameter-efficient fine-tuning (PEFT) techniques (LoRA, Prefix Tuning, QLoRA)

• Training pipelines with PyTorch Lightning / Hugging Face Transformers

• Model evaluation & benchmarking (BLEU, METEOR, CIDEr, Recall@K, VQA accuracy)

• Deployment of multimodal AI applications

• Ethical & bias-aware AI development

By the end of this course, participants will be able to:

1. Understand the architecture and functioning of Vision-Language Models.

2. Prepare multimodal datasets for fine-tuning.

3. Apply fine-tuning strategies such as full fine-tuning, LoRA, adapters, and prompt-tuning.

4. Optimize models for performance, accuracy, and efficiency.

5. Apply ethical considerations in multimodal AI.