license: cc-by-nc-sa-4.0
datasets:
- tatsu-lab/alpaca
- the_pile
Model Card for Cerebras 111M Dollyfied.
This is a finetuned model of Cerebras 111M model. using DataBricksLabs Dolly Framework
Model Details
Model Description
This is a finetuned version of cerebras' 111million paramater model that has been trained to follow instructions.
It was accomplished using DataBricks Dolly training tools and the alpaca dataset, and was trained for 2 epochs.
- 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: https://huggingface.co/cerebras/Cerebras-GPT-111m
- Finetuned using: https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html
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 suspect at best.
Direct Use
If you have a use you put it to, Please let me know.
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Downstream Use [optional]
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Out-of-Scope Use
Any form of use where any form of accuracy is needed. FOR THE LOVE OF GOD DO NOT FOLLOW MEDICAL ADVICE FROM THIS. or financial advice.
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Bias, Risks, and Limitations
Limitations... Yes, I am sure there are so so many.
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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
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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: 8xA100s (accomplished while I was downloading the model I was actually training.)
- Minutes used: 7.5
- Cloud Provider: LambdaGPU
- Compute Region: USA
- 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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Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 24.04 |
| ARC (25-shot) | 19.71 |
| HellaSwag (10-shot) | 26.68 |
| MMLU (5-shot) | 25.28 |
| TruthfulQA (0-shot) | 43.72 |
| Winogrande (5-shot) | 50.2 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 2.69 |