KoboldAI / fairseq-dense-13B-Nerys-v2

huggingface.co
Total runs: 12
24-hour runs: 0
7-day runs: -5
30-day runs: -18
Model's Last Updated: June 25 2022
text-generation

Introduction of fairseq-dense-13B-Nerys-v2

Model Details of fairseq-dense-13B-Nerys-v2

Fairseq-dense 13B - Nerys

Model Description

Fairseq-dense 13B-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>]

Revision changes:
  • Removed all headers
  • Removed all whitespaces
  • Some reshuffling of data
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-13B-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-13B-Nerys-v2 on huggingface.co

12
Total runs
0
24-hour runs
-1
3-day runs
-5
7-day runs
-18
30-day runs

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