At the intersection of artificial intelligence and code generation technology, Tsinghua University’s Knowledge Engineering Group (KEG) and data mining team have brought a revolutionary breakthrough - CodeGeeX4-ALL-9B. As the latest masterpiece of the CodeGeeX series, this model not only continues the series' outstanding performance in the field of code generation, but also sets a new industry benchmark in multilingual support and automated coding efficiency, injecting new vitality into the field of software development.
Based on the advanced GLM-4-9B framework, CodeGeeX4-ALL-9B has been carefully trained and optimized to demonstrate extraordinary code generation capabilities. The model has 940 million parameters, which is excellent in its performance among similar products, surpassing many larger-scale general models. Its excellent inference speed and comprehensive performance make it an ideal choice for various software development tasks, providing developers with strong technical support.
What is unique about CodeGeeX4-ALL-9B is its full-range functional support. From code completion, generation to code interpretation and web search, the model covers all key links of software development. In particular, its innovative warehouse-level code Q&A function allows developers to interact with the code base in a more intuitive and efficient way. This comprehensive functional design makes CodeGeeX4-ALL-9B a right-hand assistant for cross-programming environment development.
In terms of performance evaluation, CodeGeeX4-ALL-9B performed well in authoritative benchmarks such as BigCodeBench and NaturalCodeBench. These tests comprehensively evaluate the capabilities of the code generation model, and the outstanding performance of CodeGeeX4-ALL-9B fully demonstrates its reliability and practicality in real-world applications. As a leading model with a parameter size of less than 10 billion, it successfully surpasses many larger competitors.
CodeGeeX4-ALL-9B is designed with user experience in mind, ensuring developers can easily integrate it into existing workflows. By supporting a specified version of the transformers library, developers can quickly start and use the model. At the same time, the dual support of the model for GPU and CPU ensures flexible applications in different computing environments, and this accessibility will greatly promote its widespread adoption in the developer community.
In practical application scenarios, CodeGeeX4-ALL-9B's inference process demonstrates its powerful code generation ability. The model can generate clear and executable code based on user input, greatly simplifying the development process. This capability is particularly prominent in scenarios such as complex algorithm development or repetitive coding task automation, providing developers with significant efficiency improvements.
As the latest achievement of the knowledge engineering team and data mining team of Tsinghua University, the release of CodeGeeX4-ALL-9B marks an important milestone in the development of code generation technology. Its excellent performance, comprehensive feature support and user-friendly design will revolutionize the way developers handle coding tasks and promote efficiency improvements and innovative breakthroughs in the software development field.
Model address: https://huggingface.co/THUDM/codegeex4-all-9b