aihubcn/awesome-chinese-llm

整理开源的中文大语言模型,以规模较小、可私有化部署、训练成本较低的模型为主,包括底座模型,垂直领域微调及应用,数据集与教程等。

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Summary Information

Updated 54 minutes ago
Added to GitGenius on September 2nd, 2026
Created on May 22nd, 2023
Open Issues & Pull Requests: 29 (+0)
GitHub issues: Enabled
Number of forks: 2,135
Total Stargazers: 22,755 (+0)
Total Subscribers: 246 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 18.3 hours
Mean response time: 29.9 days
90th percentile: 114.8 days
Tracked items: 8

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Issue Activity (beta)

Open issues: 12
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 310 days
Stale 30+ days: 10
Stale 90+ days: 7

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

Awesome Chinese LLM is a curated list of open-source Chinese language models focused on smaller-scale, privately deployable, and cost-effective options.

The project addresses the need for accessible Chinese language model resources by organizing and cataloging open-source models that prioritize practical deployment constraints. Rather than emphasizing state-of-the-art scale, the collection centers on models suitable for private deployment with lower training costs, making them viable for organizations with limited computational resources or data sovereignty requirements.

Developers should consider this resource when building applications that require Chinese language understanding and want to avoid dependency on large proprietary models or cloud-based services. The collection is particularly suited for teams exploring fine-tuning opportunities in vertical domains, as it includes not only base models but also domain-specific adaptations, datasets, and educational materials. This makes it valuable for practitioners seeking to understand the landscape of practical Chinese LLM options rather than pursuing cutting-edge performance benchmarks.

The project maintains an organized catalog structure that reflects ongoing community engagement with the Chinese LLM ecosystem. The inclusion of base models, domain-specific fine-tuning examples, datasets, and tutorials indicates active curation across multiple dimensions of the field. The focus on smaller, deployable models suggests the maintainers are tracking practical adoption patterns rather than theoretical capabilities alone.