Codegeex4 9B - Details

Codegeex4 9B is a large language model developed by the Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University, featuring 9 billion parameters. It operates under the Glm-4-9b License (GLM-4-9B) and is designed to enhance multilingual code generation while maintaining a balance between speed and performance.

Codegeex4 9B is a multilingual code generation model trained on GLM-4-9B, designed to enhance code generation capabilities with support for code completion and generation, code interpreter, web search, function call, and repository-level code Q&A. It achieves competitive performance on benchmarks like BigCodeBench and NaturalCodeBench while maintaining a balance between inference speed and model performance. Developed by the Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University, it operates under the Glm-4-9b License (GLM-4-9B) and features 9 billion parameters.

Codegeex4 9B features 9b parameters, placing it in the mid-scale category of open-source LLMs, which offers a balanced trade-off between performance and resource efficiency for moderate complexity tasks. Its 128k context length falls into the very long contexts range, enabling advanced handling of extended texts but requiring significant computational resources. This combination allows the model to manage intricate code generation and analysis while maintaining practical inference speeds.

• Parameter Size: 9b (Mid-Scale Models: Balanced performance, handles moderate complexity)
• Context Length: 128k (Very Long Contexts: Best for very long texts, highly resource-intensive)

Codegeex4 9B is a large language model designed for code completion and generation, code interpreter, web search, function call, and repository-level code Q&A, with support for Chinese and English. Its monolingual nature may influence its adaptability to specific tasks, though possible applications could include assisting developers with coding workflows, enhancing automated code analysis, or supporting educational tools through interactive problem-solving. Possible uses might also extend to integrating web search capabilities for real-time data retrieval or enabling function calls to external systems, though these potential uses require thorough testing to ensure reliability and alignment with user needs. The model’s 9b parameter size and 128k context length suggest it is optimized for handling complex code-related tasks, but possible implementations should be carefully evaluated for specific requirements.

• code completion and generation
• code interpreter
• web search
• function call
• repository-level code q&a

Codegeex4 9B is a large language model with possible applications in areas such as code completion and generation, where its multilingual support and 9b parameter size could assist developers in writing efficient code. Possible uses might include repository-level code Q&A, enabling users to query and analyze codebases for insights. Potential applications could also involve web search integration to provide real-time data during coding tasks, or function call automation to streamline repetitive processes. These possible scenarios highlight the model’s flexibility, but each requires thorough evaluation to ensure alignment with specific needs.

• code completion and generation
• repository-level code q&a
• web search
• function call

Codegeex4 9B's medium q4 version requires a GPU with at least 16GB VRAM for efficient operation, as it falls within the 8B parameter range, where GPU is required and VRAM needs range from 12GB to 24GB. System memory should be at least 32GB, and adequate cooling and power supply are recommended. This version balances precision and performance, making it suitable for developers seeking a practical deployment option.

• fp16, q2, q3, q4, q5, q6, q8

Codegeex4 9B is a large language model developed by the Knowledge Engineering Group (KEG) & Data Mining at Tsinghua University, featuring 9 billion parameters and operating under the Glm-4-9b License. It is designed for multilingual code generation, balancing speed and performance with a 128k context length.

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