GPT Neo 1.3B Adventure

🚀 GPT-Neo 1.3B - Adventure

GPT-Neo 1.3B - Adventure is a fine - tuned model based on EleutherAI's GPT - Neo 1.3B, designed for text generation.

🚀 Quick Start

You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:

>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/GPT-Neo-1.3B-Adventure')
>>> generator("> You wake up.", do_sample=True, min_length=50)
[{'generated_text': '> You wake up"\nYou get out of bed, don your armor and get out of the door in search for new adventures.'}]

✨ Features

GPT-Neo 1.3B - Adventure is a finetune created using EleutherAI's GPT-Neo 1.3B model.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='KoboldAI/GPT-Neo-1.3B-Adventure')
>>> generator("> You wake up.", do_sample=True, min_length=50)
[{'generated_text': '> You wake up"\nYou get out of bed, don your armor and get out of the door in search for new adventures.'}]

Advanced Usage

No advanced usage examples are provided in the original document, so this part is skipped.

📚 Documentation

Limitations and Biases

GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.

BibTeX entry and citation info

The model is made using the following software:

@software{gpt-neo,
  author       = {Black, Sid and
                  Leo, Gao and
                  Wang, Phil and
                  Leahy, Connor and
                  Biderman, Stella},
  title        = {{GPT-Neo: Large Scale Autoregressive Language 
                   Modeling with Mesh-Tensorflow}},
  month        = mar,
  year         = 2021,
  note         = {{If you use this software, please cite it using 
                   these metadata.}},
  publisher    = {Zenodo},
  version      = {1.0},
  doi          = {10.5281/zenodo.5297715},
  url          = {https://doi.org/10.5281/zenodo.5297715}
}

🔧 Technical Details

No specific technical details are provided in the original document, so this section is skipped.

📄 License

This project is licensed under the MIT license.