README.md

---
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-Greentext-125m
A finetuned version of [GPT-Neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on the 'greentext' dataset. (Linked above)
Do also take a look at [GPT-Greentext-1.5b](https://huggingface.co/DarwinAnim8or/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](https://huggingface.co/spaces/DarwinAnim8or/GPT-Greentext-Playground)
The demo playground is recommended over the inference box on the right, as it uses the largest model in this series. 

# Training Procedure
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.

# 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
```python
#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)
```