Model Type | text generation, code synthesis |
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Use Cases |
Areas: | |
Applications: | Code synthesis, Code understanding, Python specific tasks |
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Primary Use Cases: | Commercial code generation, Research projects in programming and AI |
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Limitations: | Not for non-English languages, Should not violate laws or regulations |
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Considerations: | Adhere to legal compliances. |
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Additional Notes | Intended for Python code tasks; requires specific handling for different operations. |
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Supported Languages | English (proficient), Python (proficient) |
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Training Details |
Data Sources: | Same data as Llama 2 with different weights |
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Methodology: | Fine-tuned with additional instruction data for CodeLlama - Instruct |
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Context Length: | |
Training Time: | |
Hardware Used: | Metaβs Research Super Cluster, A100-80GB GPUs |
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Model Architecture: | Autoregressive language model using optimized transformer architectures |
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Responsible Ai Considerations |
Fairness: | Tested primarily in English. |
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Transparency: | Model's potential outputs cannot be predicted in advance. |
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Accountability: | Developers should perform safety testing and tuning tailored to their specific applications before deployment. |
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Mitigation Strategies: | Please see the Responsible Use Guide for more details. |
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Input Output |
Input Format: | |
Accepted Modalities: | |
Output Format: | |
Performance Tips: | Use 'trust_remote_code=True' for optimal operation due to a change in the RoPE Theta value |
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