metadata

license: mit
datasets:
  - DarwinAnim8or/greentext
language:
  - en
tags:
  - fun
  - greentext
widget:
  - text: '>be me'
    example_title: be me
co2_eq_emissions:
  emissions: 60
  source: https://mlco2.github.io/impact/#compute
  training_type: fine-tuning
  geographical_location: Oregon, USA
  hardware_used: 1 T4, Google Colab

GPT-DMV-125m

A finetuned version of GPT-Neo-125M on the 'greentext' dataset. (Linked above) A demo is available here

Training Procedure

This was trained on the 'grug' dataset, using the "HappyTransformers" library on Google Colab. This model was trained for 15 epochs with learning rate 1e-2.

Biases & Limitations

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.

Intended Use

This model is meant for fun, nothing else.

Sample Use

#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=3, do_sample=True,top_k=80, temperature=0.4, max_length=50, early_stopping=False)

#Generate a response:
result = happy_gen.generate_text(""">be me
>""", args=args_top_k)

print(result)
print(result.text)