Python, PyTorch, Hugging Face Transformers, Deep Learning, Vision Transformers, Multimodal Large Language Models (MLLMs), Efficient Fine-Tuning
Résumé/CV – Master’s Research Project (2025)
- Developed a parameter-efficient adaptation strategy for frozen MLLMs by learning pixel-space visual prompts that steer the model without modifying its weights.
- Designed and executed experiments on multiple public vision datasets using PyTorch, Hugging Face Transformers, and custom vision-language integration pipelines.
- Achieved performance competitive with full fine-tuning while updating less than 0.001% of total model parameters (e.g., 52K vs. 7B), demonstrating strong generalization and efficiency.
Remote: Yes
Willing to relocate: Yes
Technologies:
Python, PyTorch, Hugging Face Transformers, Deep Learning, Vision Transformers, Multimodal Large Language Models (MLLMs), Efficient Fine-Tuning
Résumé/CV – Master’s Research Project (2025)
- Developed a parameter-efficient adaptation strategy for frozen MLLMs by learning pixel-space visual prompts that steer the model without modifying its weights.
- Designed and executed experiments on multiple public vision datasets using PyTorch, Hugging Face Transformers, and custom vision-language integration pipelines.
- Achieved performance competitive with full fine-tuning while updating less than 0.001% of total model parameters (e.g., 52K vs. 7B), demonstrating strong generalization and efficiency.
- Paper and code available soon.
Email: [email protected]