---
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.

[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

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### 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

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#### Factors

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

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#### Metrics

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

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### Results

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#### Summary



## Model Examination [optional]

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

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## 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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## 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:**

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**APA:**

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## Glossary [optional]

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

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

| 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         |