
# Model Card for Cerebras 590M Dollyfied.

This is a finetuned model of Cerebras 590M model. using DataBricksLabs Dolly Framework

## Model Details

### Model Description

This is a finetuned version of cerebras' 590million 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-590m
- **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]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[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

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Corianas__Quokka_590m)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 25.02   |
| ARC (25-shot)         | 24.4          |
| HellaSwag (10-shot)   | 31.61    |
| MMLU (5-shot)         | 25.36         |
| TruthfulQA (0-shot)   | 39.59   |
| Winogrande (5-shot)   | 50.2   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 3.96         |

{"language": ["en"], "license": "apache-2.0", "datasets": ["the_pile", "guanaco/guanaco"]}
transformers pytorch tensorboard safetensors gpt2 text-generation en dataset:the_pile dataset:guanaco/guanaco arxiv:1910.09700 license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #en #dataset-the_pile #dataset-guanaco/guanaco #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

Model Card for Cerebras 590M Dollyfied.
=======================================


This is a finetuned model of Cerebras 590M model. using DataBricksLabs Dolly Framework


Model Details
-------------


### Model Description


This is a finetuned version of cerebras' 590million 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]:
* Model type:
* Language(s) (NLP): EN
* License: cc-by-nc-4.0
* Finetuned from model: URL
* Finetuned using: URL


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.


### Downstream Use [optional]


### 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.


Bias, Risks, and Limitations
----------------------------


Limitations... Yes, I am sure there are so so many.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
----------------


### Training Data


### Training Procedure


#### Preprocessing [optional]


#### Training Hyperparameters


* Training regime:


#### Speeds, Sizes, Times [optional]


Evaluation
----------


### Testing Data, Factors & Metrics


#### Testing Data


#### Factors


#### Metrics


### Results


#### Summary


Model Examination [optional]
----------------------------


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:


Technical Specifications [optional]
-----------------------------------


### Model Architecture and Objective


### Compute Infrastructure


#### Hardware


#### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------


Open LLM Leaderboard Evaluation Results
=======================================


Detailed results can be found here




### Model Description


This is a finetuned version of cerebras' 590million 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]:
* Model type:
* Language(s) (NLP): EN
* License: cc-by-nc-4.0
* Finetuned from model: URL
* Finetuned using: URL


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. ### Downstream Use [optional] ### 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.


Bias, Risks, and Limitations
----------------------------


Limitations... Yes, I am sure there are so so many.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
---------------- ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters


* Training regime: #### Speeds, Sizes, Times [optional]


Evaluation
---------- ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


Model Examination [optional]
----------------------------


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:


Technical Specifications [optional]
----------------------------------- ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------


Open LLM Leaderboard Evaluation Results
=======================================


Detailed results can be found here
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #en #dataset-the_pile #dataset-guanaco/guanaco #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Model Description


This is a finetuned version of cerebras' 590million 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]:
* Model type:
* Language(s) (NLP): EN
* License: cc-by-nc-4.0
* Finetuned from model: URL
* Finetuned using: URL


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. ### Downstream Use [optional] ### 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.


Bias, Risks, and Limitations
----------------------------


Limitations... Yes, I am sure there are so so many.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
---------------- ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters


* Training regime: #### Speeds, Sizes, Times [optional]


Evaluation
---------- ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary


Model Examination [optional]
----------------------------


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:


Technical Specifications [optional]
----------------------------------- ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------


Open LLM Leaderboard Evaluation Results
=======================================


Detailed results can be found here
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #en #dataset-the_pile #dataset-guanaco/guanaco #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
### Model Description


This is a finetuned version of cerebras' 590million 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]:
* Model type:
* Language(s) (NLP): EN
* License: cc-by-nc-4.0
* Finetuned from model: URL
* Finetuned using: URL


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.### Downstream Use [optional]### 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.


Bias, Risks, and Limitations
----------------------------


Limitations... Yes, I am sure there are so so many.


How to Get Started with the Model
---------------------------------


Use the code below to get started with the model.


Training Details
----------------### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters


* Training regime:#### Speeds, Sizes, Times [optional]


Evaluation
----------### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary


Model Examination [optional]
----------------------------


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:


Technical Specifications [optional]
-----------------------------------### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software


[optional]


BibTeX:


APA:


Glossary [optional]
-------------------


More Information [optional]
---------------------------


Model Card Authors [optional]
-----------------------------


Model Card Contact
------------------


Open LLM Leaderboard Evaluation Results
=======================================


Detailed results can be found here