| Model Type | | text generation, code-related tasks |
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| Use Cases |
| Areas: | | enterprise software engineering productivity |
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| Applications: | | code generation, code explanation, code fixing, generating unit tests, generating documentation, addressing technical debt issues, vulnerability detection, code translation |
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| Primary Use Cases: | | support for 128K context length tasks |
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| Limitations: | | Generated code is not guaranteed to work as intended., Has not undergone any safety alignment, potential for problematic outputs., Potential increased susceptibility to hallucination in smaller models. |
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| Considerations: | | Urge ethical use and responsibly check outputs for accuracy and reliability. |
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| Supported Languages | | language_proficiency (/** languages and their proficiency levels **/), proficiencies (Python, C, C++, Go, Java, JavaScript, TypeScript) |
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| Training Details |
| Data Sources: | | codeparrot/github-code-clean, bigcode/starcoderdata, open-web-math/open-web-math, math-ai/StackMathQA |
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| Data Volume: | |
| Methodology: | | Continual pretraining and repository-level file packing with per-language length upsampling. |
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| Context Length: | |
| Hardware Used: | | NVIDIA A100 GPUs, NVIDIA H100 GPUs |
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| Model Architecture: | | Progressively adjusted RoPE theta |
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| Responsible Ai Considerations |
| Mitigation Strategies: | | Caution urged against complete reliance on generated code; not undergone any safety alignment, could produce problematic outputs. |
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| Release Notes | |