Downloads · 30 days
103
0% of all-time downloads
hakurei/lit-6B
lit-6B is a text generation model from hakurei. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
Downloads · 30 days
103
0% of all-time downloads
All-time downloads
285K
Public
Repo size
24.2 GB
Likes
74
Public
Click a slice to open those files.
.bin12.1 GB · 100%
From the Hugging Face model README
Lit-6B is a GPT-J 6B model fine-tuned on 2GB of a diverse range of light novels, erotica, and annotated literature for the purpose of generating novel-like fictional text.
The model used for fine-tuning is GPT-J, which is a 6 billion parameter auto-regressive language model trained on The Pile.
The data used in fine-tuning has been gathered from various sources such as the Gutenberg Project. The annotated fiction dataset has prepended tags to assist in generating towards a particular style. Here is an example prompt that shows how to use the annotations.
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror; Tags: 3rdperson, scary; Style: Dark ]
***
When a traveler in north central Massachusetts takes the wrong fork...
The annotations can be mixed and matched to help generate towards a specific style.
This model can be used for entertainment purposes and as a creative writing assistant for fiction writers.
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained('hakurei/lit-6B')
tokenizer = AutoTokenizer.from_pretrained('hakurei/lit-6B')
prompt = '''[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler'''
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, do_sample=True, temperature=1.0, top_p=0.9, repetition_penalty=1.2, max_length=len(input_ids[0])+100, pad_token_id=tokenizer.eos_token_id)
generated_text = tokenizer.decode(output[0])
print(generated_text)
An example output from this code produces a result that will look similar to:
[ Title: The Dunwich Horror; Author: H. P. Lovecraft; Genre: Horror ]
***
When a traveler comes to an unknown region, his thoughts turn inevitably towards the old gods and legends which cluster around its appearance. It is not that he believes in them or suspects their reality—but merely because they are present somewhere else in creation just as truly as himself, and so belong of necessity in any landscape whose features cannot be altogether strange to him. Moreover, man has been prone from ancient times to brood over those things most connected with the places where he dwells. Thus the Olympian deities who ruled Hyper
This project would not have been possible without the computational resources graciously provided by the TPU Research Cloud