111m / README.md
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Adding Evaluation Results (#2)
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metadata
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.

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]

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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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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

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