# Distilled GPT-2 Story Generation Model (June 2025)

This is a distilled version of GPT-2, fine-tuned using knowledge distillation from a teacher model (Qwen3-1.7B) on the ROCStories dataset. The model is designed for story generation with constraint words.

## Model Details
- **Base Model**: GPT-2
- **Teacher Model**: Qwen3-1.7B
- **Dataset**: ROCStories (Ximing/ROCStories)
- **Training Objective**: Knowledge distillation to match teacher outputs.
- **Training Date**: June 12, 2025
- **Evaluation**: Constraint word inclusion success rate.

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "here4code/distilled-gpt2-story-generation-Qwen3-1.7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Once upon a time, there was a happy dog"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details
- **Epochs**: 2
- **Batch Size**: 128
- **Training Loss per Epoch**: [np.float64(4.636714812309023), np.float64(2.7680806659516835)]

## Evaluation Results
- **Teacher Constraint Word Inclusion Success Rate**: 0.8500
- **Student Constraint Word Inclusion Success Rate**: 0.9000

## License
This model is released under the MIT License.