Red.Whimsy_Sweet Model Card

Model Summary

Red.Whimsy_Sweet is an AI model fine-tuned for creative and long-form roleplaying interactions. It explores NSFW and controversial themes, supporting up to 9,000 words per generation. Based on EleutherAI/gpt-neo-1.3B, it uses datasets from Wikipedia and custom roleplaying datasets for immersive dialogue and storytelling.

How to Use the Model

To use Red.Whimsy_Sweet, follow the instructions below.

Install Required Libraries

pip install transformers torch datasets gradio

Load the Model and Create a Gradio Interface

from transformers import GPTNeoForCausalLM, GPT2Tokenizer
import gradio as gr

# Load model and tokenizer
model = GPTNeoForCausalLM.from_pretrained("BleuHydr4nge4/red-whimsy-sweet")
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")

# Generate text
def generate_text(prompt):
    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
    output = model.generate(input_ids, max_length=9000, do_sample=True, temperature=0.7)
    return tokenizer.decode(output[0], skip_special_tokens=True)

# Gradio Interface
iface = gr.Interface(
    fn=generate_text, 
    inputs="text", 
    outputs="text", 
    title="Red.Whimsy_Sweet AI",
    description="Generate creative and long-form roleplaying text with Red.Whimsy_Sweet."
)

# Launch the interface
iface.launch()

Deploy on Hugging Face Hub

To deploy your model on the Hugging Face Hub, use the following commands:

huggingface-cli login
transformers-cli upload ./path_to_your_model --repo_name red-whimsy-sweet

Example Usage

Here's an example of how to use the model programmatically:

prompt = "In a world of ancient magic, a lone traveler sets off on a perilous journey..."
response = generate_text(prompt)
print(response)

Limitations and Ethical Considerations

• This model may generate content that is not suitable for all audiences due to its NSFW capabilities.
• The model's outputs should be reviewed and filtered for inappropriate or offensive content before use in production environments.
• Users should be aware of potential biases in the model's training data and outputs.

License

This model is released under the BSD 3-Clause License. Please see the license file for more details.