This repository provides free, open-source online courses from MindSpore, focusing on Large Language Models (LLMs). It targets developers interested in LLMs, offering a blend of theoretical explanations and hands-on coding guidance from industry experts. The courses aim to demystify LLM technology, from foundational concepts like Transformers to practical applications and advanced tuning techniques.

How It Works

The courses are structured thematically, covering key LLM architectures (Transformer, BERT, GPT, LLaMA), advanced training and fine-tuning methods (Prompt Tuning, RLHF, LoRA), and specific model implementations (ChatGLM, CodeGeex, CPM-Bee, RWKV, Mixtral). Each lecture typically includes video, presentation slides, and accompanying code, often leveraging the MindSpore framework and its distributed training capabilities. The content progresses from foundational principles to cutting-edge research and practical deployment.

Quick Start & Requirements

• Installation: Primarily involves cloning the repository and following individual lecture instructions for code execution.
• Prerequisites: Python, MindSpore framework, MindFormers, and potentially other libraries depending on the specific lecture (e.g., Hugging Face Transformers, PyTorch for comparison). Access to computing resources (GPU recommended for practical exercises) is implied.
• Resources: Links to MindSpore tutorials, MindFormers documentation, and the OpenI启智社区 for compute resources are provided.

Highlighted Details

• Comprehensive coverage of LLM evolution, from Transformer to ChatGPT and beyond.
• Hands-on coding sessions for building simplified LLMs and deploying various models.
• Explanations of advanced techniques like distributed training, prompt tuning, RLHF, and quantization.
• Focus on the MindSpore ecosystem, including its automatic parallelization features.
• Includes lectures on code generation models and multimodal LLMs.

Maintenance & Community

The project is actively maintained by the MindSpore team, with course content and code regularly updated. Community engagement is encouraged through GitHub issues for feedback and suggestions, and a QQ group is used for course announcements.

Licensing & Compatibility

Course materials and code are open-source, with specific licenses likely aligning with the MindSpore ecosystem (typically Apache 2.0 or similar permissive licenses), allowing for commercial use and integration.

Limitations & Caveats

Some lectures may have incomplete code or presentation materials ("更新中", "/"). The course structure and schedule are subject to change, with official announcements made via QQ groups and the MindSpore public account. Practical exercises may require significant computational resources.