Quantization made by Richard Erkhov.
GPT-Neo 2.7B-Picard is a finetune created using EleutherAI's GPT-Neo 2.7B model.
The training data contains around 1800 ebooks, mostly in the sci-fi and fantasy genres.
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
1>>> from transformers import pipeline
2>>> generator = pipeline('text-generation', model='mrseeker87/GPT-Neo-2.7B-Picard')
3>>> generator("Jean-Luc Picard", do_sample=True, min_length=50)
4[{'generated_text': 'Jean-Luc Picard, the captain of a Federation starship in command of one of Starfleet's few fulltime scientists.'}]
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.
1@software{gpt-neo,
2 author = {Black, Sid and
3 Leo, Gao and
4 Wang, Phil and
5 Leahy, Connor and
6 Biderman, Stella},
7 title = {{GPT-Neo: Large Scale Autoregressive Language
8 Modeling with Mesh-Tensorflow}},
9 month = mar,
10 year = 2021,
11 note = {{If you use this software, please cite it using
12 these metadata.}},
13 publisher = {Zenodo},
14 version = {1.0},
15 doi = {10.5281/zenodo.5297715},
16 url = {https://doi.org/10.5281/zenodo.5297715}
17}
