Introduction
Pretrained code generation models are very new and transformative tools in software development. These models can do everything from code snippet generation to translation of whole functions and code translation across languages. However, some of the remaining issues to date include the handing of diverse programming languages, staying in context over long ranges, and guaranteeing the correctness of the generated code.
The latest in this line is CodeGeeX4. The joint work of Tsinghua University and Zhipu AI, CodeGeeX4 has fixed these problems and made gigantic improvements, thanks to feedback from the AI research community. In developing CodeGeeX4, researcher's core motivation was to build a strong multilingual code generation model that performs well on general software development tasks, ranging from code completion to repository-level Q&A.
What is CodeGeeX4?
CodeGeeX4, also known as CodeGeeX4-ALL-9B (part of same model series), is an open-source multilingual code generation model. It represents the latest in the CodeGeeX series and has been continually trained on the GLM-4-9B framework. This continuous training has significantly enhanced its capabilities, enabling it to generate and interpret code across multiple programming languages with improved efficiency and accuracy.
Key Features of CodeGeeX4
CodeGeeX4 comes with several unique features that set it apart from other models in the field:
- Multilingual Support: CodeGeeX4 supports a wide range of programming languages, making it a versatile tool for developers around the globe.
- Enhanced Context Handling: With a context length of up to 128K tokens, CodeGeeX4 can manage extensive codebases and maintain context over long sequences.
- Comprehensive Functions: The model supports a variety of functions such as code completion, generation, interpretation, web search, function calls, and repository-level Q&A.
- Performance: CodeGeeX4 achieves competitive performance on benchmarks like BigCodeBench and NaturalCodeBench, surpassing many larger models in terms of inference speed and accuracy.
source - https://github.com/THUDM/CodeGeeX4
Capabilities/Use Case of CodeGeeX4
The capabilities of CodeGeeX4 extend beyond just code generation. It can be used in a wide range of software development scenarios, thanks to its comprehensive functions:
- Code Completion and Generation: CodeGeeX4 can predict and generate code snippets, helping developers write code faster and with fewer errors.
- Code Interpretation: The model can interpret existing code, providing explanations and summaries.
- Web Search and Function Calls: CodeGeeX4 integrates web search capabilities and can generate function calls based on user queries.
- Repository-Level Q&A: CodeGeeX4 can answer questions related to code repositories, making it a valuable tool for large projects.
These capabilities make CodeGeeX4 a versatile tool that can handle a wide range of software development scenarios. This makes it a valuable tool for developers.
Architecture of base CodeGeeX model
CodeGeeX4 is a cutting-edge multilingual code generation model that leverages an innovative architecture designed for efficient autoregressive programming tasks. CodeGeeX4 is the latest version in the CodeGeeX series.
source - https://arxiv.org/pdf/2303.17568
CodeGeeX is built on the generative pre-training (GPT) architecture, similar to models like GPT-3, PaLM, and Codex. It employs a decoder-only style for autoregressive language modeling. The core of CodeGeeX is a 39-layer transformer decoder, where each layer applies a multi-head self-attention mechanism followed by MLP layers. This is complemented by layer normalization and residual connections. CodeGeeX also uses an approximation of the GELU operation, known as FastGELU, which is more efficient under the Ascend 910 AI Processor.
The model is trained on a large amount of unlabeled code data, following the GPT paradigm. It takes code tokens as input, predicts the next token, and compares it with the ground truth. This process is iteratively performed to optimize the cumulative cross-entropy loss. CodeGeeX also features a top query layer, which replaces the original GPT model’s pooler function. This layer obtains the final embedding through attention and the output probability is obtained by multiplying the final output by the transpose of the word embedding matrix. CodeGeeX supports various decoding strategies, including greedy, temperature sampling, top-k sampling, top-p sampling, and beam search. The selected token ID is then detokenized into an actual word.
Performance Evaluation
CodeGeeX4-ALL-9B has demonstrated exceptional performance on various benchmarks, establishing itself as a leading code generation model with less than 10 billion parameters. On BigCodeBench, it scored 48.9 and 40.4 for the complete and instruct tasks, respectively, outperforming many larger models. This benchmark evaluates the model’s ability to generate and complete code snippets across diverse programming languages, highlighting CodeGeeX4’s robust multilingual capabilities and efficiency.
source - https://github.com/THUDM/CodeGeeX4
In the NaturalCodeBench and HumanEval benchmarks, CodeGeeX4-ALL-9B continues to excel. NaturalCodeBench, designed to reflect real-world coding scenarios, includes 402 high-quality problems in Python and Java. CodeGeeX4’s performance on these tasks underscores its practical utility in handling complex coding challenges. Additionally, on HumanEval, which focuses on code synthesis and completion, CodeGeeX4-ALL-9B achieved competitive scores, further validating its effectiveness in generating accurate and contextually relevant code.
source - https://github.com/THUDM/CodeGeeX4
Beyond these benchmarks, CodeGeeX4-ALL-9B also excels in specialized tasks such as Code Needle In A Haystack, Function Call Capabilities, and Cross-File Completion. In the Needle In A Haystack evaluation, it achieved a 100% retrieval accuracy within contexts up to 128K tokens. It is also the only model supporting function call capabilities, with a better execution success rate than GPT-4. Furthermore, its cross-file completion capabilities enhance its utility in large-scale projects, enabling it to handle dependencies and related files effectively.
CodeGeeX4-All-9B: A Cut Above the Rest
When comparing CodeGeeX4-All-9B with Llama3-70B-instruct, DeepSeek Coder 33B Instruct, and Codestral-22B, several key differences and advantages of CodeGeeX4-All-9B come to the fore. While Llama3-70B-instruct is a large language AI model optimized for dialogue use cases, and DeepSeek Coder 33B Instruct is trained from scratch on a mix of code and natural language, CodeGeeX4-All-9B sets itself apart with its multilingual support and continual training on the GLM-4-9B. This continual training allows CodeGeeX4-All-9B to constantly learn and adapt, potentially leading to improved performance over time.
Codestral-22B, on the other hand, is designed specifically for code generation tasks and uses a fill-in-the-middle (FIM) mechanism. However, CodeGeeX4-All-9B supports a wider range of functions, including code completion, generation, interpretation, web search, function call, and repository-level code Q&A. This wide range of capabilities could make CodeGeeX4-All-9B more adaptable and effective at handling various tasks, leading to better performance on benchmarks like HumanEval.
So, while all four models have their unique strengths and capabilities, CodeGeeX4-All-9B’s multilingual support, continual training, comprehensive functionality, and highly competitive performance make it a standout model in the field of AI and code generation. Its ability to perform well on the HumanEval benchmark demonstrates its effectiveness and versatility, making it a valuable tool for a wide range of software development scenarios.
How to Access and Use CodeGeeX4
CodeGeeX4 is accessible on multiple platforms, including GitHub, Hugging Face, and its official website. As an open-source model, it is available for both research and commercial use. For local deployment, detailed instructions are provided to integrate the model with Visual Studio Code or JetBrains extensions.
To ensure users can effectively utilize CodeGeeX4-ALL-9B, comprehensive user guides are available. These guides cover various functionalities and usage scenarios, offering a thorough understanding of the model. Detailed descriptions and instructions can be found on the GitHub repository, facilitating efficient and effective use of the model.
If you would like to read more details about this AI model, the sources are all included at the end of this article in the 'source' section.
Limitations
Here are some potential limitations of the CodeGeeX4 model:
- Contextual Understanding: Like other AI models, CodeGeeX4 might struggle with understanding the context of certain code generation tasks. It might not always generate the most efficient or optimal code for complex tasks.
- Dependency on Training Data: The performance of CodeGeeX4 is heavily dependent on the quality and diversity of its training data. If the training data is biased or lacks representation for certain types of code or programming tasks, the model might underperform in those areas.
- Real-time Performance: While CodeGeeX4-ALL-9B has achieved a good balance in terms of inference speed and model performance, real-time performance could still be a challenge, especially for larger code generation tasks.
- Security and Privacy: As with any AI model, there could be potential security and privacy concerns. For instance, if sensitive information is included in the code, the model needs to handle it appropriately.
Please note that the actual performance and limitations can vary based on the specific use case and implementation.
Conclusion
As we bridge the gap between technical prowess and real-world application, this multilingual code generation model stands out for its versatility, performance, and continual learning. CodeGeeX4-All-9B’s robust capabilities extend beyond mere code generation. It interprets, completes, and answers, empowering developers across diverse programming languages. Its exceptional performance on benchmarks like HumanEval underscores its effectiveness, making it an invaluable tool for software development scenarios.
Source
research document base model : https://arxiv.org/pdf/2303.17568
wisemodel website: https://wisemodel.cn/models/ZhipuAI/codegeex4-all-9b
GitHub Repo: https://github.com/THUDM/CodeGeeX4
Hugging Face Repo: https://huggingface.co/THUDM/codegeex4-all-9b
Website: https://codegeex.cn/en-US
Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.
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