DarwinAnim8or/greentext language:
en tags:
fun
greentext widget:
text: '>be me' example_title: be me
text: '>be zoo keeper' co2_eq_emissions: emissions: 10 source: https://mlco2.github.io/impact/#compute training_type: fine-tuning geographical_location: Oregon, USA hardware_used: 1x T4, Google Colab

Pythia-Greentext-1.4b

A finetuned version of Pythia-1.4b on the 'greentext' dataset. A demo is available here The demo playground is recommended over the inference box on the right.

This is an alternate take on my "GPT-Greentext" releases.

Training Procedure

This was trained on the 'greentext' dataset, on Google Colab. This model was trained for 1 epoch with learning rate 1e-2. Notably this uses the "prompt" and "completion" style jsonl file, rather than the plain text file found in the greentext dataset. This nets somewhat better, mostly more consistent results.

Biases & Limitations

This likely contains the same biases and limitations as the original model that it is based on, and additionally heavy biases from the greentext dataset. It should be noted that offensive or not PG-output is definitely possible and likely will happen.

Intended Use

This model is meant for fun, nothing else.

Noteworthy differences between this model and the others

This model tends to like no_repeat_ngram_size values of 1 or 2; whereas the other models in this series tend to prefer 3.

Sample Use

python

#Import model:
from happytransformer import HappyGeneration
happy_gen = HappyGeneration("GPTNEO", "DarwinAnim8or/Pythia-Greentext-1.4b")

#Set generation settings:
from happytransformer import GENSettings
args_top_k = GENSettingsGENSettings(no_repeat_ngram_size=2, do_sample=True, top_k=80, temperature=0.1, 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)

RichardErkhov/DarwinAnim8or_-_Pythia-Greentext-1.4b-awq

Original model description: