license: apache-2.0
datasets:
- the_pile
- guanaco/guanaco
language:
- en
Model Card for Cerebras 2.7b Dollyfied.
This is a finetuned model of Cerebras 2.7b model. using DataBricksLabs Dolly Framework
Model Details
Model Description
This is a finetuned version of cerebras' 2.7Billion paramater model that has been trained to follow instructions.
It was accomplished using DataBricks Dolly training tools, 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.
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
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.
[More Information Needed]
Bias, Risks, and Limitations
Limitations... Yes, I am sure there are so so many.
[More Information Needed]
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
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: 25
- Cloud Provider: LambdaGPU
- Compute Region: USA
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 29.72 |
| ARC (25-shot) | 31.06 |
| HellaSwag (10-shot) | 47.72 |
| MMLU (5-shot) | 24.8 |
| TruthfulQA (0-shot) | 40.14 |
| Winogrande (5-shot) | 55.49 |
| GSM8K (5-shot) | 0.38 |
| DROP (3-shot) | 8.43 |