This was trained on the 'grug' dataset, using the "HappyTransformers" library on Google Colab. This model was trained for 4 epochs with learning rate 1e-2. The notebook used to train has been included in this repo.

Biases & Limitations

This likely contains the same biases and limitations as the original GPT2 that it is based on, and additionally heavy biases from the grug datasets.

Intended Use

This model is meant for fun, please do not take anything this caveman says seriously.

Sample Use

python

#Import model:
from happytransformer import HappyGeneration
happy_gen = HappyGeneration("GPT2", "DarwinAnim8or/gpt-grug-355m")

#Set generation settings:
from happytransformer import GENSettings
args_top_k = GENSettings(no_repeat_ngram_size=2, do_sample=True,top_k=50, temperature=0.7, max_length=50, early_stopping=False)

#Generate a response:
result = happy_gen.generate_text("""Person: "Hello grug"
Grug: "hello person"
###
Person: "how are you grug"
Grug: "grug doing ok. grug find many berry. good for tribe."
###
Person: "what does grug think of new spear weapon?"
Grug: "grug no like new spear weapon. grug stick bigger. spear too small, break easy"
###
Person: "what does grug think of football?"
Grug: \"""", args=args_top_k)

print(result)
print(result.text)

DarwinAnim8or/GPT-Grug-355m

GPT-Grug-355m

Training Procedure

Biases & Limitations

Intended Use

Sample Use

DarwinAnim8or/
GPT-Grug-355m