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README.md
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tags:
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- medical
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---
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# Model Card for
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<!-- Provide a quick summary of what the model is/does. -->
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@@ -76,6 +76,28 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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- **Cloud Provider:** [Azure]
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- **Compute Region:** [North Europe (Dublin)]
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## Technical Specifications [optional]
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tags:
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- medical
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---
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# Model Card for Phi-Med-V1
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<!-- Provide a quick summary of what the model is/does. -->
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- **Cloud Provider:** [Azure]
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- **Compute Region:** [North Europe (Dublin)]
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\usepackage{hyperref}
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\subsection{CO2 Emission Related to Experiments}
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Experiments were conducted using Azure in region northeurope, which has a carbon efficiency of 0.62 kgCO$_2$eq/kWh. A cumulative of 100 hours of computation was performed on hardware of type RTX 3090 (TDP of 350W).
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Total emissions are estimated to be 21.7 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
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%Uncomment if you bought additional offsets:
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%XX kg CO2eq were manually offset through \href{link}{Offset Provider}.
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Estimations were conducted using the \href{https://mlco2.github.io/impact#compute}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.
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@article{lacoste2019quantifying,
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title={Quantifying the Carbon Emissions of Machine Learning},
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author={Lacoste, Alexandre and Luccioni, Alexandra and Schmidt, Victor and Dandres, Thomas},
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journal={arXiv preprint arXiv:1910.09700},
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year={2019}
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}
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## Technical Specifications [optional]
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