Model Type | |
Use Cases |
Areas: | |
Applications: | Software development, Code completion |
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Primary Use Cases: | Generating code snippets with provided context |
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Limitations: | Cannot guarantee working code, May produce inefficient or bug-prone code |
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Considerations: | Model is trained on open-source code, ensure adherence to licensing. |
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Additional Notes | Model pretraining included filtering for permissive licenses and can generate code verbatim, requiring license compliance. |
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Supported Languages | Python (High), Java (High), JavaScript (High) |
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Training Details |
Data Sources: | GitHub code filtered for permissive licenses |
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Data Volume: | |
Methodology: | Filled for permissive licenses; uses multi-query attention and Fill-in-the-Middle objective |
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Training Time: | |
Hardware Used: | |
Model Architecture: | GPT-2 model with multi-query attention |
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Input Output |
Input Format: | Model expects code-like inputs along with comments or function signatures. |
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Accepted Modalities: | |
Output Format: | |
Performance Tips: | Ensure inputs are appropriately structured to resemble typical source code prompts. |
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