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# 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
```sh
pip install transformers torch
```
### Loading the Model
```python
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:
| **Metric** | **Score** | **Meaning** |
|-------------|-----------|-------------|
| **ROUGE-1** | **0.7525** (~75%) | Measures overlap of **unigrams (single words)** between the reference and generated text. |
| **ROUGE-2** | **0.3552** (~35%) | Measures overlap of **bigrams (two-word phrases)**, indicating coherence and fluency. |
| **ROUGE-L** | **0.4904** (~49%) | Measures **longest matching word sequences**, testing sentence structure preservation. |
| **ROUGE-Lsum** | **0.5701** (~57%) | Similar to ROUGE-L but optimized for storytelling tasks. |
## 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.
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