Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
Model Card for Model ID
Model Details
Model Description
- Developed by: [Amreesh Kumar]
- Funded by [optional]: [Ram Milan]
- Shared by [optional]: [Amreesh Kumar]
- Model type: [full-Stack Developer]
- Language(s) (NLP): [Python and more...]
- License: [Belongs to idiot-developer]
Uses
This model is trained for generating complete MERN stack applications and it can make full production-ready projects with authentication, admin panels, database models, API routes, and responsive frontend components without token limit.
This model is trained for creating enterprise-level web applications and it can make entire e-commerce platforms with shopping carts, user management, payment integration, and admin dashboards without token limit.
This model is trained for building full-stack JavaScript solutions and it can make complete React frontends with Tailwind CSS, Express.js backends with MongoDB, and real-time features without token limit.
This model is trained for rapid prototyping and MVP development and it can make functional startup applications with user authentication, database CRUD operations, and professional UI/UX without token limit.
This model is trained for professional web development workflows and it can make deployment-ready codebases with environment configuration, error handling, security measures, and optimized performance without token limit.
This model is trained for educational code generation and it can make comprehensive learning examples with proper documentation, code comments, and industry best practices without token limit.
This model is trained for custom business applications and it can make specialized solutions for healthcare, education, finance, real estate, and restaurant industries without token limit.
Direct Use
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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
Use the code below 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
Testing Data
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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).
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Technical Specifications [optional]
Model Architecture and Objective
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Hardware
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Software
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Framework versions
- PEFT 0.17.1