Downloads · 30 days
23
4% of all-time downloads
DarwinAnim8or/GPT-Greentext-125m
GPT-Greentext-125m is a text generation model from DarwinAnim8or. 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.
A finetuned version of GPT-Neo-125M on the 'greentext' dataset. (Linked above) Do also take a look at GPT-Greentext-1.5b, the larger size model of this project, it will produce better-quality greentexts than this mode…
Downloads · 30 days
23
4% of all-time downloads
All-time downloads
618
Public
Parameters
176M
1.1 GB on disk
Likes
0
Public
Click a slice to open those files.
.bin551 MB · 50%
How the weights are stored.
F32125M · 71%
From the Hugging Face model README
A finetuned version of GPT-Neo-125M on the 'greentext' dataset. (Linked above) Do also take a look at GPT-Greentext-1.5b, the larger size model of this project, it will produce better-quality greentexts than this model can. A demo is available here The demo playground is recommended over the inference box on the right, as it uses the largest model in this series.
This was trained on the 'greentext' dataset, using the "HappyTransformers" library on Google Colab. This model was trained for 15 epochs with learning rate 1e-2.
This likely contains the same biases and limitations as the original GPT-Neo-125M that it is based on, and additionally heavy biases from the greentext dataset. It likely will generate offensive output.
This model is meant for fun, nothing else.
#Import model:
from happytransformer import HappyGeneration
happy_gen = HappyGeneration("GPT-NEO", "DarwinAnim8or/GPT-Greentext-125m")
#Set generation settings:
from happytransformer import GENSettings
args_top_k = GENSettings(no_repeat_ngram_size=2, do_sample=True,top_k=80, temperature=0.4, max_length=150, early_stopping=False)
#Generate a response:
result = happy_gen.generate_text(""">be me
>""", args=args_top_k)
print(result)
print(result.text)