

Codegeex4 ALL 9B
Overview :
CodeGeeX4-ALL-9B is the latest open-source version of the CodeGeeX4 series of models. Based on continuous training of GLM-4-9B, it has significantly enhanced code generation capabilities. It supports functions such as code completion, generation, code interpretation, web search, function calling, and code question answering, covering multiple scenarios in software development. It performs excellently on public benchmark tests such as BigCodeBench and NaturalCodeBench, becoming the strongest code generation model with less than 10 billion parameters, achieving the best balance between inference speed and model performance.
Target Users :
Designed for software developers, programming educators, and researchers, especially professionals who need to quickly generate code, understand code logic, manage code repositories, and answer coding questions.
Use Cases
Developers use CodeGeeX4-ALL-9B to quickly complete and generate code, improving development efficiency.
Educators utilize the model for programming education, helping students understand complex code structures.
Researchers use the model for academic research and benchmark testing related to code generation.
Features
Code Completion and Generation: Supports automatic completion and generation of code in various programming languages.
Code Interpreter: Can understand and interpret code snippets, providing logical and functional explanations of code execution.
Web Search: Integrated search functionality to help users quickly find relevant information.
Function Calling: Supports function-level code calling and execution.
Code Question Answering: Provides question-and-answer functionality at the code repository level to help solve programming problems.
Multi-turn Dialogue History Maintenance: Maintains context information through system prompts to improve interaction quality.
Code Retrieval: Retrieves code in large-scale contexts, achieving high-accuracy code location.
How to Use
1. Install necessary Python libraries, such as transformers.
2. Obtain the tokenizer from THUDM/codegeex4-all-9b using AutoTokenizer.
3. Load the CodeGeeX4-ALL-9B model using AutoModelForCausalLM.
4. Prepare input data and perform tokenization using the tokenizer.
5. Set the model to evaluation mode and execute code generation.
6. Utilize the model's output results for subsequent code usage or analysis.
7. Refer to the user guide for an in-depth understanding of the model's advanced usage.
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