Model Type | |
Use Cases |
Areas: | research, commercial applications |
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Applications: | code generation, code reasoning, code fixing, code agents |
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Primary Use Cases: | coding capabilities, mathematics, general competencies |
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Limitations: | Not recommended for conversations |
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Considerations: | Post-training or specific task tuning is recommended for certain applications |
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Additional Notes | The model's architecture includes RoPE, SwiGLU, RMSNorm, and Attention QKV bias. |
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Supported Languages | |
Training Details |
Data Sources: | source code, text-code grounding, synthetic data |
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Data Volume: | |
Methodology: | transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias architecture |
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Context Length: | |
Model Architecture: | transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
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Input Output |
Input Format: | Supports up to 128K tokens in context length. |
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Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use 'rope_scaling' for handling long contexts optimally. |
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