Purpose
awesome-LLMs-In-China is a list repository focused on information about Chinese large models, aiming to comprehensively collect information on large models with clear sources, including institutions, source information, and classifications, etc. The repository aims to record the development of Chinese large models and welcomes leads and materials provided in Issues.
Main Capabilities
- Collect and organize relevant information on Chinese large models, covering institutions, source information, and classifications, etc.
- Stay updated at all times to reflect the latest developments of Chinese large models.
- Provide links to related projects in the Awesome series, including large model evaluation datasets and lists of open-source and open foundational large models.
How to Use
- Visit the GitHub repository https://github.com/wgwang/awesome-LLMs-In-China to view the list contents.
- Provide leads and materials through Issues to participate in contributions.
- When using the data, please indicate the source: the WeChat public account "走向未来" and the repository link.
Applicable Scenarios
- For researchers, developers, or people interested in artificial intelligence to understand the development of Chinese large models.
- As a reference resource for information related to large models.
Limitations
- The repository is an information list and does not provide the models themselves or direct usage functionality.
- Specific content must be obtained by visiting the GitHub repository; other details are not yet provided.
Other Information
The repository is also associated with the WeChat public account "走向未来", which shares AI-related content such as large models, AGI, knowledge graphs, deep learning, reinforcement learning, computer vision, and natural language processing. In addition, the repository maintainer recommends the book "Knowledge Graph: Cognitive Intelligence Theory and Practice", which introduces knowledge graph construction, storage, and application technologies, as well as content such as Transformers and graph neural networks, and is one of the foundational technologies of large models.
