CodeGeeX4-ALL-9B

CodeGeeX4-ALL-9B is the latest open source version of the CodeGeeX4 series model. Based on GLM-4-9B continuous training, it significantly improves code generation capabilities. It supports code completion, generation, code interpretation, web search, function calling, code Q&A and other functions, covering multiple scenarios of software development. It performs well on public benchmarks such as BigCodeBench and NaturalCodeBench. It is the strongest code generation model with less than 1 billion parameters, achieving the best balance between inference speed and model performance.

Demand group:

"For software developers, programming educators, and researchers, especially professionals who need to quickly generate code, understand code logic, code base management, and Q&A."

Example of usage scenario:

Developers use CodeGeeX4-ALL-9B to quickly complete and generate code to improve development efficiency.

Educators use models to teach programming and help students understand complex code structures.

Researchers use the model for academic research and benchmarking related to code generation.

Product features:

Code completion and generation: Supports automatic code completion and generation for multiple programming languages.

Code interpreter: Able to understand and interpret code segments, providing logical and functional explanations of code execution.

Web search: Integrated search function to help users quickly find relevant information.

Function call: Supports function-level code calling and execution.

Code Q&A: Provides Q&A functionality at the code base level to help solve programming problems.

Multi-turn dialogue history maintenance: Maintain contextual information and improve interaction quality through system prompt guidance.

Code retrieval: Retrieve code in large-scale context to achieve high-accuracy code location.

Usage tutorial:

1. Install necessary Python libraries such as transformers.

2. Use AutoTokenizer to obtain the tokenizer from THUDM/ CodeGeeX4-ALL-9B .

3. Use AutoModelForCausalLM to load the CodeGeeX4-ALL-9B model.

4. Prepare input data and use tokenizer for word segmentation processing.

5. Set the model to evaluation mode and perform code generation.

6. Use the model output results for subsequent code use or analysis.

7. If needed, refer to the user guide to learn more about advanced usage of the model.