This is a collection of Jupyter notebooks that serve as educational guides for training, fine-tuning, and deploying machine learning models within the Hugging Face ecosystem. The notebooks cover the full lifecycle of model development, from loading and configuring pre-trained transformers to packaging trained models for real-time inference via scalable endpoints.
The notebooks demonstrate a range of capabilities including diffusion model training and fine-tuning for image generation and editing, transformer model adaptation for natural language processing tasks, and parameter-efficient fine-tuning techniques that reduce computational cost. They also cover multi-GPU training orchestration, hardware accelerator utilisation, and the deployment of models as production inference endpoints.
Beyond core training workflows, the collection includes guides for image generation tasks such as text-to-image synthesis, inpainting, super-resolution, and instruction-based editing. Additional notebooks cover robot policy training from demonstration data and long-form question answering systems using retrieval-augmented approaches. The repository also provides tooling for converting static documentation into executable notebooks for interactive learning.
