
# 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)
```
{"language": ["en"], "license": "mit", "tags": ["fun", "greentext"], "datasets": ["DarwinAnim8or/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"}}
transformers pytorch safetensors gpt_neo text-generation fun greentext en dataset:DarwinAnim8or/greentext license:mit co2_eq_emissions autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #fun #greentext #en #dataset-DarwinAnim8or/greentext #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 


# GPT-Greentext-125m
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. 

# 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

# GPT-Greentext-125m
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. # 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
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #fun #greentext #en #dataset-DarwinAnim8or/greentext #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
 # GPT-Greentext-125m
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. # 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
TAGS
#transformers #pytorch #safetensors #gpt_neo #text-generation #fun #greentext #en #dataset-DarwinAnim8or/greentext #license-mit #co2_eq_emissions #autotrain_compatible #endpoints_compatible #region-us 
# GPT-Greentext-125m
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.# 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