Model Card for Model ID
This is a finetuned model of Cerebras 256M model using DataBricksLabs Dolly Framework
Model Details
Model Description
- Developed by: Finetuned by Corianas (me) using open source tools
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): EN
- License: cc-by-nc-4.0
- Finetuned from model [optional]: https://huggingface.co/cerebras/Cerebras-GPT-256M
Uses
This is a simple GPT chatbot that has been finetuned to understand instructions.
Its knowledge about facts about the world is should be considered almost non-existant.
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
- Training regime: [More Information Needed]
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
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: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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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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Detailed results can be found
here| Metric | Value |
|---|
| Avg. | 24.88 |
| ARC (25-shot) | 22.87 |
| HellaSwag (10-shot) | 28.84 |
| MMLU (5-shot) | 26.48 |
| TruthfulQA (0-shot) | 39.47 |
| Winogrande (5-shot) | 52.25 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 4.27 |
