Model Card for Model ID
Deca 2.5 Pro is...
Model Details
Model Description
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- Developed by: Deca AI w/ GenLabs AI
- Funded by: GenLabs AI
- Model type: DynAMoE (Sparse Dynamically Activated Mixture of Experts)
- Languages: [More Information Needed]
- License: MIT
Model Sources [optional]
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- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
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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
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).
- Hardware Type: [More Information Needed]
- Hours used: ~10M hours (approx)
- Compute Region: Decentralized
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
Sparse Dynamically Activated Mixture of Experts
Compute Infrastructure
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Hardware Requirements
CPU:
Best CPU possible
As much RAM as possible (at least 256GB, ideally 1TB)
4TB SSD as fast as possible
GPU:
4x B200 or 8x H100 80G
4TB SSD
Total VRAM + RAM should be as high as possible
Software
vLLM, transformers
Citation [optional]
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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