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