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---
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language: en
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license: mit
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---
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# GPT-J 6B - Shinen
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## Model Description
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GPT-J 6B-Shinen is a finetune created using EleutherAI's GPT-J 6B model. Compared to GPT-Neo-2.7-Horni, this model is much heavier on the sexual content.
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*Warning: THIS model is NOT suitable for use by minors. The model will output X-rated content.*
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## Training data
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The training data contains user-generated stories from sexstories.com. All stories are tagged using the following way:
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```
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[Theme: <theme1>, <theme2> ,<theme3>]
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<Story goes here>
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```
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### How to use
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You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
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```py
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>>> from transformers import pipeline
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>>> generator = pipeline('text-generation', model='KoboldAI/GPT-J-6B-Shinen')
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>>> generator("She was staring at me", do_sample=True, min_length=50)
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[{'generated_text': 'She was staring at me with a look that said it all. She wanted me so badly tonight that I wanted'}]
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```
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### Limitations and Biases
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The core functionality of GPT-J 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. When prompting GPT-J it is important to remember that the statistically most likely next token is often not the token that produces the most "accurate" text. Never depend upon GPT-J to produce factually accurate output.
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GPT-J was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending upon use case GPT-J may produce socially unacceptable text. See [Sections 5 and 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a more detailed analysis of the biases in the Pile.
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As with all language models, it is hard to predict in advance how GPT-J 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.
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### BibTeX entry and citation info
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The model uses the following model as base:
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```bibtex
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@misc{gpt-j,
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author = {Wang, Ben and Komatsuzaki, Aran},
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title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
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howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
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year = 2021,
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month = May
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}
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```
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## Acknowledgements
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This project would not have been possible without compute generously provided by Google through the
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[TPU Research Cloud](https://sites.research.google/trc/), as well as the Cloud TPU team for providing early access to the [Cloud TPU VM](https://cloud.google.com/blog/products/compute/introducing-cloud-tpu-vms) Alpha.
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