DeepSeek-Coder

功能特性

• Massive Training Data: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
• Highly Flexible & Scalable: Offered in model sizes of 1B, 5.7B, 6.7B and 33B, enabling users to choose the setup most suitable for their requirements.
• Superior Model Performance: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
• Advanced Code Completion Capabilities: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
• Step 1: Collect code data from GitHub and apply the same filtering rules as StarCoder Data to filter data.
• Step 2: Parsing the dependencies of files within the same repository to rearrange the file positions based on their dependencies.

项目预览

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