CodeShell Technical Report

• URL: http://arxiv.org/abs/2403.15747v1
• Date: Sat, 23 Mar 2024 07:29:41 GMT
• Title: CodeShell Technical Report
• Authors: Rui Xie, Zhengran Zeng, Zhuohao Yu, Chang Gao, Shikun Zhang, Wei Ye,
• Abstract summary: We present CodeShell-Base, a foundation model with 8K context length, showcasing exceptional proficiency in code comprehension. We have curated 100 billion high-quality pre-training data from GitHub. Benefiting from the high-quality data, CodeShell-Base outperforms CodeLlama in Humaneval after training on just 500 billion tokens (5 epochs)
• Score: 23.741490720927068
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code large language models mark a pivotal breakthrough in artificial intelligence. They are specifically crafted to understand and generate programming languages, significantly boosting the efficiency of coding development workflows. In this technical report, we present CodeShell-Base, a seven billion-parameter foundation model with 8K context length, showcasing exceptional proficiency in code comprehension. By incorporating Grouped-Query Attention and Rotary Positional Embedding into GPT-2, CodeShell-Base integrates the structural merits of StarCoder and CodeLlama and forms its unique architectural design. We then carefully built a comprehensive data pre-processing process, including similar data deduplication, perplexity-based data filtering, and model-based data filtering. Through this process, We have curated 100 billion high-quality pre-training data from GitHub. Benefiting from the high-quality data, CodeShell-Base outperforms CodeLlama in Humaneval after training on just 500 billion tokens (5 epochs). We have conducted extensive experiments across multiple language datasets, including Python, Java, and C++, and the results indicate that our model possesses robust foundational capabilities in code comprehension and generation.

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