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    All product names, logos, and brands are the property of their respective owners. All company, product, and service names used in this repository, related repositories, and associated websites are for identification purposes only. The use of these names, logos, and brands does not imply endorsement, affiliation, or sponsorship. This directory may include content generated by artificial intelligence.
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    1. Home
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    3. Awesome Chinese LLM

    Awesome Chinese LLM

    An awesome-style curated list of open-source Chinese large language models, focused on smaller-scale models suitable for private deployment, along with domain-specific fine-tunes, applications, datasets, and tutorials.

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    Websitegithub.com
    PublishedDec 25, 2025

    Categories

    1 Item
    Themed Directories

    Tags

    3 Items
    #ai#llm#open-source

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    Awesome Chinese LLM

    URL: https://github.com/HqWu-HITCS/Awesome-Chinese-LLM
    Category: Themed Directories
    Tags: ai, llm, open-source

    Overview

    Awesome Chinese LLM is a curated, "awesome-style" directory of open-source Chinese large language model resources. It emphasizes smaller-scale models that are feasible for private deployment and lower-cost training, and aggregates base models, domain-specific fine-tunes, applications, datasets, and tutorials related to Chinese LLMs.

    Features

    1. Focus and Scope

    • Concentrates on Chinese-language LLMs and related tooling.
    • Prioritizes smaller-scale models that:
      • Can be run by individuals or small teams.
      • Are suitable for private deployment.
      • Have lower training and deployment costs.
    • Includes resources across the full ecosystem:
      • Base / foundation models.
      • Domain-specific fine-tuned models.
      • Applications built on top of LLMs.
      • Datasets for training and evaluation.
      • Tutorials and learning materials.

    2. Base Model Overview Table

    Provides a comparative overview of commonly used base models, including:

    • ChatGLM family

      • Variants: ChatGLM / ChatGLM2 / ChatGLM3 / ChatGLM4 (Base & Chat)
      • Parameters: ~6B
      • Training tokens: ~1T / 1.4T
      • Max sequence length: 2K / 32K
      • Commercial use: Allowed
    • LLaMA family

      • Variants: LLaMA / LLaMA2 / LLaMA3 (Base & Chat)
      • Parameters: 7B / 8B / 13B / 33B / 70B
      • Training tokens: ~1T / 2T
      • Max sequence length: 2K / 4K
      • Commercial use: Partially allowed (depends on version/license)
    • Baichuan

      • Variants: Baichuan / Baichuan2 (Base & Chat)
      • Parameters: 7B / 13B
      • Training tokens: ~1.2T / 1.4T
      • Max sequence length: 4K
      • Commercial use: Allowed
    • Qwen (通义千问)

      • Variants: Qwen / Qwen1.5 / Qwen2 / Qwen2.5 (Base, Chat, VL)
      • Parameters: 7B / 14B / 32B / 72B / 110B
      • Training tokens: ~2.2T / 3T / 18T
      • Max sequence length: 8K / 32K
      • Commercial use: Allowed
    • BLOOM

      • Variants: BLOOM
  • Parameters: 1B / 7B / 176B-MT
  • Training tokens: ~1.5T
  • Max sequence length: 2K
  • Commercial use: Allowed
  • Aquila

    • Variants: Aquila / Aquila2 (Base / Chat)
    • Parameters: 7B / 34B
    • Max sequence length: 2K
    • Commercial use: Allowed
  • InternLM

    • Variants: InternLM / InternLM2 / InternLM2.5 (Base / Chat / VL)
    • Parameters: 7B / 20B
    • Max sequence length: up to 200K
    • Commercial use: Allowed
  • Mixtral

    • Variants: Base & Chat
    • Parameters: 8×7B (Mixture-of-Experts)
    • Max sequence length: 32K
    • Commercial use: Allowed
  • Yi

    • Variants: Base & Chat
    • Parameters: 6B / 9B / 34B
    • Training tokens: ~3T
    • Max sequence length: up to 200K
    • Commercial use: Allowed
  • DeepSeek

    • Variants: Base & Chat
    • Parameters: 1.3B / 7B / 33B / 67B
    • Max sequence length: 4K
    • Commercial use: Allowed
  • XVERSE

    • Variants: Base & Chat
    • Parameters: 7B / 13B / 65B / A4.2B
    • Training tokens: ~2.6T / 3.2T
    • Max sequence length: 8K / 16K / 256K
    • Commercial use: Allowed
  • 3. Structured Directory of Resources

    The repository is organized as an "awesome list" with a table of contents that includes (among others):

    • 1. 模型 (Models)
      • 1.1 文本 LLM 模型 (Text LLMs)
      • 1.2 多模态 LLM 模型 (Multimodal LLMs)

    (Additional sections for applications, datasets, and tutorials exist but are not fully visible in the provided excerpt.)

    4. Scale and Community Activity

    • Tracks and collects 100+ Chinese LLM-related open-source resources.
    • Hosted as a public GitHub repository, allowing community contributions via pull requests.
    • As of the snapshot, shows significant community interest (stars and forks), indicating active maintenance and ecosystem relevance.

    5. Contribution Guidelines (High-Level)

    • Encourages contributions of:
      • New open-source models.
      • Applications built on Chinese LLMs.
      • Datasets and tutorials.
    • Requests contributors to follow a consistent format, including:
      • Repository link.
      • Star count.
      • Concise introduction/description.

    Pricing

    • This is an open-source, free GitHub directory.
    • No pricing plans or paid tiers are indicated.