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Apply for a GPU community grant: Academic project
I am requesting GPU community resources to support educational and pedagogical projects in the field of Deep Learning at ENSIM Engineering School, Le Mans University (France).
As a faculty member teaching Deep Learning courses and supervising engineering student projects, I aim to provide students with hands-on experience in modern artificial intelligence workflows, including model training, fine-tuning, evaluation, and deployment using state-of-the-art deep learning frameworks and open-source tools.
The objective of this project is to create a shared Hugging Face Space environment where students can experiment with GPU-accelerated deep learning projects as part of their academic coursework and engineering projects.
The proposed GPU resources will be used for:
Training and fine-tuning deep learning models (computer vision, natural language processing, multimodal learning, and generative AI).
Experimenting with modern architectures such as transformers, diffusion models, and large language model adaptation techniques.
Developing interactive demonstrations and prototypes using Hugging Face Spaces.
Teaching practical skills in reproducible AI development, open-source AI tools, and responsible AI practices.
Supporting student projects where computational resources are often a major limitation.
Educational Context:
ENSIM Engineering School trains engineering students in computer science, electronics, and industrial digital technologies. Within my Deep Learning courses, students learn both theoretical foundations and practical implementation skills.
The availability of GPU resources through Hugging Face would significantly improve the quality of these projects by allowing students to work with realistic models and datasets rather than only simplified examples constrained by local hardware.
The GPU-enabled Hugging Face Space will serve as a collaborative educational platform where students can:
Build and share AI applications.
Learn modern machine learning workflows.
Reproduce research ideas from the scientific community.
Publish educational demonstrations and open-source examples.
A collection of student-developed Hugging Face Spaces demonstrating deep learning applications.
Open educational resources that can be reused in future courses.
Student projects exploring current AI research topics.
Increased student engagement with open-source AI ecosystems.
The resulting work will remain primarily educational and will contribute to the open AI learning community by sharing examples, tutorials, and demonstrations created by engineering students.
Why GPU Support is Needed:
Deep learning education requires access to computational resources. Many modern models cannot be effectively trained or fine-tuned using standard student laptops or CPU-only environments.
Access to GPU resources through this grant would allow students to gain practical experience with realistic AI workloads while maintaining an accessible and equitable learning environment for all students.
The requested GPU support will be used exclusively for academic and pedagogical purposes within Deep Learning courses and supervised engineering projects at ENSIM, Le Mans University.
This initiative aligns with Hugging Face’s mission of democratizing machine learning by enabling students and educators to access powerful AI tools and participate in the open-source AI community.
By providing GPU access, Hugging Face will directly support the next generation of AI engineers and researchers, helping students move from theoretical understanding to practical innovation.