Instructions to use shapermindai/code-gemma-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use shapermindai/code-gemma-7b with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("shapermindai/code-gemma-7b", set_active=True) - Notebooks
- Google Colab
- Kaggle
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- **Compute Region:** US
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- **Carbon Emitted:** [More Information Needed]
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Experiments were conducted using Google Cloud Platform in region northamerica-northeast1, which has a carbon efficiency of 0.
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Total emissions are estimated to be 0.08 kgCO
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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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- **Compute Region:** US
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- **Carbon Emitted:** [More Information Needed]
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Experiments were conducted using Google Cloud Platform in region northamerica-northeast1, which has a carbon efficiency of 0.03kgCO^2/kWh. A cumulative of 10.5 hours of computation was performed on hardware of type A100 PCIe 40/80GB (TDP of 250W).
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Total emissions are estimated to be 0.08 kgCO^2 of which 100 percents were directly offset by the cloud 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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