| Model Type | | code generation, decoder-only, text-generation |
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| Use Cases |
| Areas: | | enterprise use, 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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| Limitations: | | Risks of problematic outputs, No safety alignment, Increased susceptibility to hallucination |
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| Considerations: | | Caution against complete reliance for crucial decisions |
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| Supported Languages | | 116 programming languages (comprehensive) |
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| Training Details |
| Data Sources: | | Publicly available datasets from GitHub Code Clean, Starcoder data |
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| Data Volume: | | 3 trillion tokens (Phase 1), 500 billion tokens (Phase 2) |
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| Methodology: | | Two-phase training strategy (comprehensive understanding, improved reasoning) |
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| Hardware Used: | | IBM's Vela and Blue Vela supercomputing clusters, NVIDIA A100 and H100 GPUs |
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| Model Architecture: | |
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| Safety Evaluation |
| Risk Categories: | | malicious utilization, unsafe code generation |
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| Ethical Considerations: | | The generated code is not guaranteed to work as intended, risks of malicious use. |
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| Responsible Ai Considerations |
| Mitigation Strategies: | | HAP, PII, Malware Filtering |
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| Release Notes |
| Date: | |
| Notes: | | Model released with decoder-only architecture suited for code generative tasks. |
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