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README.md

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+# GPT-Neo 2.7B - Picard
+## Model Description
+GPT-Neo 2.7B-Picard is a finetune created using EleutherAI's GPT-Neo 2.7B model.
+## Training data
+The training data contains around 1800 ebooks, mostly in the sci-fi and fantasy genres.
+### 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:
+```py
+>>> from transformers import pipeline
+>>> generator = pipeline('text-generation', model='mrseeker87/GPT-Neo-2.7B-Picard')
+>>> generator("Jean-Luc Picard", do_sample=True, min_length=50)
+[{'generated_text': 'Jean-Luc Picard, the captain of a Federation starship in command of one of Starfleet's few fulltime scientists.'}]
+```
+### 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:
+```bibtex
+@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}
+}
+```