A hands-on tutorial for efficiently developing and deploying deep learning models.
- Deep learning model development: model design, model training & evaluation.
- LLM development: data curation, model pre-training, instruction fine-tuning, reinforcement-based alignment.
- Diffusion development: data curation, model pre-training, efficient fine-tuning.
- Deep learning model deployment: model optimization, inference server.
- LLM optimizations: vLLM, SGLang, TensorRT-LLM.
- Diffusion optimizations: diffusers, OneDiff.
- Model optimizations: hardware-aware optimizations, LLM-specific optimizations, Diffusion-specific optimizations.
- An interactive demo for deep learning models: Gradio, Streamlit, React.
- Multimodal understanding: multimodal models, basic vision tasks, advanced vision tasks.
- LLM applications: video understanding, recommendation systems, best practices.
If you wish to publish your work on open-source community, the following resources are much helpful.
- Lightning-AI. "Deep Learning Project Template (Code)". Github repo.
- eliahuhorwitz. "Academic Project Page Template (Website)". Github repo.
- mintlify. "The starter kit for your Mintlify docs (Product Spec)". Github repo.
For insights into efficient on-device AIGC algorithms and systems, check out my blogs.