Quantifying the Carbon Emissions of Machine Learning
Paper
• 1910.09700 • Published
• 36
Model Card for Model ID
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Model Details
Model Description
- Developed by: [AxiomAI-comunnity]
- Funded by [GenAI/Fantominsight]: [AxiomAI]
- Shared by [GenAI/Fantominsight]: [AxiomAI]
- Model type: [LLM AI, transformers, 3B]
- Language(s) (NLP): [English, Russian, Chinese]
- License: [MIT]
- Finetuned from model [none]: [not Fine-tuned]
Model Sources [optional]
- Repository: [this repository]
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Uses
Direct Use
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
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Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [GPU-server]
- Hours used: [0]
- Cloud Provider: [Hugging Face]
- Compute Region: [Pekin China, Moscow Russia]
- Carbon Emitted: [Not provided now. Try again later]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Software
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Citation [optional]
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More Information [optional]
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Model Card Authors [optional]
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Model Card Contact
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