Getting Started with Large Model Development on IONET

This course involves the use of artificial intelligence. Some images in the course are AI-generated, and AI has also been used for translation and content correction of certain audio segments.

Our course is built around open platforms such as ionet, vastAI, and RunPod, offering a practical and affordable pathway into large model development. Using the cost-effective ionet as a primary example, we guide learners through the full lifecycle of deploying and utilizing large language models, with knowledge that seamlessly transfers across platforms.

This course places a strong emphasis on open-source large model development, empowering students to move beyond API-based usage and gain hands-on experience with models like Llama, TongYi. You’ll learn how to deploy models locally and in the cloud, perform inference with tools like Hugging Face Transformers and vLLM, apply quantization for efficiency, and fine-tune models using LoRA for specific tasks.

We cover essential topics including basic inference, Retrieval-Augmented Generation (RAG), Agent workflows, and the management of cloud resources such as virtual machines, containers, and GPU orchestration. Real-world projects include building AI agents, implementing RAG systems for enterprise knowledge bases, and optimizing models for production deployment.

In an era where AI tools like Cursor promote “Vibe Coding” and the rapid evolution of large models creates uncertainty, mastering open-source LLM development provides clarity and competitive advantage. This course equips developers and students with the technical depth, practical skills, and confidence to thrive in the new era of AI.