This repository serves as a structured curriculum and resource hub for learning about and applying large language models (LLMs) and visual-language models (VLMs), specifically focusing on the InternLM ecosystem. It targets individuals seeking practical, hands-on experience with LLM development, fine-tuning, deployment, and integration, offering a guided path from foundational concepts to advanced applications.

How It Works

The project is organized into a series of "levels" or "challenges" that progressively build knowledge and skills. Each challenge provides access to tasks, documentation, and video tutorials. The curriculum covers essential prerequisites like Linux and Python, platform usage (Hugging Face, ModelScope), prompt engineering, Retrieval-Augmented Generation (RAG) with LlamaIndex, model fine-tuning with XTuner, evaluation with OpenCompass, multi-modal model deployment, and agent development with Lagent.

Quick Start & Requirements

The primary way to engage is through the linked wiki: https://aicarrier.feishu.cn/wiki/QtJnweAW1iFl8LkoMKGcsUS9nld. Specific technical requirements are detailed within each challenge, but generally involve access to computing resources (e.g., GPU instances for fine-tuning and deployment) and familiarity with common AI development platforms.

Highlighted Details

• Structured learning path from beginner to advanced topics.
• Focus on the InternLM suite of LLMs and related tools (XTuner, LMDeploy, InternVL).
• Practical application areas include RAG, multi-agent systems, and multi-modal AI.
• Incentive program offering compute credits and platform access for participation and referrals.

Maintenance & Community

The project is associated with the InternLM initiative. Further community engagement details are not explicitly provided in the README, but a "InternLM Co-learning Plan" encourages sharing and collaboration.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The content is presented as a tutorial series, implying it is educational rather than a production-ready library. Specific technical prerequisites and setup instructions are distributed across various challenge modules, requiring users to navigate the linked resources.