Fairseq-dense 2.7B-Nerys is a finetune created using Fairseq's MoE dense model.
Training data
The training data contains around 2500 ebooks in various genres (the "Pike" dataset), a CYOA dataset called "CYS" and 50 Asian "Light Novels" (the "Manga-v1" dataset).
Most parts of the dataset have been prepended using the following text:
[Genre: <genre1>, <genre2>]
How to use
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/fairseq-dense-2.7B-Nerys')
>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
Limitations and Biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
BibTeX entry and citation info
Artetxe et al. (2021): Efficient Large Scale Language Modeling with Mixtures of Experts
Runs of KoboldAI fairseq-dense-2.7B-Nerys on huggingface.co
100
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24-hour runs
3
3-day runs
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7-day runs
32
30-day runs
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