gpt2-lmheadmodel-story-telling-model

GPT-2 for Storytelling

This repository hosts a quantized version of the GPT-2 model, fine-tuned for creative writing and storytelling tasks. The model has been optimized for efficient deployment while maintaining high coherence and creativity, making it suitable for resource-constrained environments.

Model Details

• Model Architecture: gpt2-lmheadmodel-story-telling-model
• Task: Storytelling & Writing Prompts Generation
• Dataset: euclaise/writingprompts
• Quantization: Float16
• Fine-tuning Framework: Hugging Face Transformers

Usage

Installation

pip install transformers torch

Loading the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "AventIQ-AI/gpt2-lmheadmodel-story-telling-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

import html

# Define test text
test_text = "Once upon a time, in a mystical land,"

# Tokenize input
inputs = tokenizer(test_text, return_tensors="pt").to(device)

# Generate response
with torch.no_grad():
    output_tokens = model.generate(
        **inputs,
        max_length=200, 
        num_beams=5,
        repetition_penalty=2.0, 
        temperature=0.7, 
        top_k=50, 
        top_p=0.9, 
        do_sample=True, 
        no_repeat_ngram_size=3, 
        num_return_sequences=1,  
        early_stopping=True, 
        length_penalty=1.2,  
        pad_token_id=tokenizer.eos_token_id, 
        eos_token_id=tokenizer.eos_token_id, 
        return_dict_in_generate=True,  
        output_scores=True  
    )

# Decode and clean response
generated_response = tokenizer.decode(output_tokens.sequences[0], skip_special_tokens=True)
cleaned_response = html.unescape(generated_response).replace("#39;", "'").replace("quot;", '"')

print("\nGenerated Response:\n", cleaned_response)

📊 ROUGE Evaluation Results

After fine-tuning the GPT-2 model for storytelling, we obtained the following ROUGE scores:

Fine-Tuning Details

Dataset

The Hugging Face euclaise/writingprompts dataset was used, containing creative writing prompts and responses.

Training

• Number of epochs: 3
• Batch size: 4
• Evaluation strategy: epoch
• Learning rate: 5e-5

Quantization

Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Quantized Model
├── README.md            # Model documentation

Limitations

• The model may not generalize well to domains outside the fine-tuning dataset.
• Quantization may result in minor accuracy degradation compared to full-precision models.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.