GitHub

ChatGLM3

What is the project about?

ChatGLM3 is a family of open-source dialogue language models, co-developed by Zhipu AI and Tsinghua University's KEG Lab. It's the third generation of the ChatGLM series, designed to be a powerful and versatile conversational AI. It builds upon the strengths of its predecessors while introducing new features and improvements.

What problem does it solve?

  • Provides a strong, open-source alternative to closed-source large language models (LLMs). This allows researchers and developers to freely use, study, and modify the model without restrictive licensing.
  • Lowers the barrier to entry for deploying and using powerful conversational AI. It's designed to be relatively easy to deploy, even on consumer-grade hardware.
  • Offers a more capable base model for further research and development. The improved base model (ChatGLM3-6B-Base) provides a stronger foundation for fine-tuning and specialized applications.
  • Addresses the limitations of smaller LLMs in terms of accuracy and reliability. While still a 6B parameter model, it strives to improve the quality and trustworthiness of generated content.
  • Provides long-context understanding. ChatGLM3-6B-32K and ChatGLM3-6B-128K models are specifically designed to handle longer conversations and documents.

What are the features of the project?

  • Stronger Base Model: ChatGLM3-6B-Base outperforms previous versions and other models in its size class on various benchmarks (semantics, math, reasoning, code, knowledge).
  • Multiple Model Variants:
    • ChatGLM3-6B: The main conversational model.
    • ChatGLM3-6B-Base: The foundation model, suitable for fine-tuning.
    • ChatGLM3-6B-32K: Handles longer contexts (up to 32,000 tokens).
    • ChatGLM3-6B-128K: Handles even longer contexts (up to 128,000 tokens).
  • Tool Use (Function Calling): Natively supports calling external tools/APIs to perform actions and retrieve information.
  • Code Interpreter: Can execute code within a Jupyter environment to solve complex problems.
  • Agent Capabilities: Can be used for more complex agent-based tasks.
  • New Prompt Format: A redesigned prompt structure for better control and flexibility.
  • Open Source: Weights are fully open for academic research, and free commercial use is allowed after registration.
  • Multiple Deployment Options: Supports various deployment methods, including:
    • Standard Hugging Face Transformers library.
    • Quantization for reduced memory usage.
    • CPU deployment.
    • Mac (MPS) deployment.
    • Multi-GPU deployment.
    • OpenVINO for Intel CPUs and GPUs.
    • TensorRT-LLM for NVIDIA GPUs.
  • Integration with Frameworks: Works with popular frameworks like LangChain.
  • OpenAI API Compatibility: Can be deployed as a backend for ChatGPT-based applications.
  • Customizable Tools: Support for custom tools.

What are the technologies used in the project?

  • Python: The primary programming language.
  • PyTorch: The deep learning framework.
  • Transformers (Hugging Face): The library used for loading and interacting with the model.
  • Gradio/Streamlit: For creating web-based demos.
  • LangChain: For building applications with LLMs.
  • OpenVINO (optional): For optimized inference on Intel hardware.
  • TensorRT-LLM (optional): For optimized inference on NVIDIA hardware.
  • Git LFS: For managing large model files.
  • Jupyter: For the Code Interpreter functionality.
  • FastAPI: For creating the OpenAI-compatible API.

What are the benefits of the project?

  • Openness and Accessibility: Promotes research and development in the open-source community.
  • Cost-Effectiveness: Can be deployed on relatively low-resource hardware, reducing costs.
  • Flexibility: Supports various use cases and deployment scenarios.
  • Improved Performance: Offers better performance compared to previous generations and similar-sized models.
  • Extensibility: Can be fine-tuned and extended with custom tools and functionalities.
  • Community Support: Benefits from contributions and support from the open-source community.
  • Commercial Use: Free for commercial use after registration.

What are the use cases of the project?

  • Chatbots and Conversational AI: Building interactive dialogue systems.
  • Question Answering: Answering questions based on provided context or general knowledge.
  • Text Summarization: Summarizing long documents or conversations.
  • Code Generation and Assistance: Generating code snippets or helping with programming tasks.
  • Content Creation: Generating creative text formats, like poems, code, scripts, musical pieces, email, letters, etc.
  • Research: Studying and advancing the field of large language models.
  • Tool Integration: Creating applications that leverage external tools and APIs.
  • Agent-Based Systems: Developing intelligent agents that can perform complex tasks.
  • Long-Context Applications: Analyzing and processing long documents, such as research papers, financial reports, or legal documents.
  • Knowledge Base: Building RAG (Retrieval-Augmented Generation) knowledge base.
ChatGLM3 screenshot