Model Card for bt-opt-350m

Model Details

Model Description

More information needed

Developed by: Opentensor
Shared by [Optional]: Opentensor
Model type: Text Generation
Language(s) (NLP): More information needed
License: Other
Parent Model: OPT
Resources for more information: More information needed

Uses

Direct Use

This model can be used for the task of Text Generation

Downstream Use [Optional]

More information needed.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

More information needed

Training Procedure

Preprocessing

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Speeds, Sizes, Times

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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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Model Examination

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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: More information needed
Hours used: More information needed
Cloud Provider: More information needed
Compute Region: More information needed
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

BibTeX:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

Opentensor in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

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How to Get Started with the Model

Use the code below to get started with the model.

Click to expand

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("opentensor/bt-opt-350m")

model = AutoModelForCausalLM.from_pretrained("opentensor/bt-opt-350m")