README.md

---
license: mit
language:
- en
tags:
- text-generation
- gpt2
- stories
- children-stories
- tinystories
datasets:
- roneneldan/TinyStories
widget:
- text: "Once upon a time"
  example_title: "Story Beginning"
- text: "The little girl loved to"
  example_title: "Character Story"
- text: "In a magical forest,"
  example_title: "Fantasy Setting"
---

# RonMicro-LLM-Story (Phase 1)

A small GPT-2 style language model trained on TinyStories dataset for generating children's stories.

## Model Details

- **Model Type:** GPT-2 Causal Language Model
- **Parameters:** ~40M
- **Training Data:** TinyStories (5% subset, ~105K stories)
- **Vocabulary Size:** 25,913 tokens
- **Context Length:** 512 tokens
- **Training Epochs:** 3
- **Language:** English

## Training Details

- **Framework:** Transformers (Hugging Face)
- **Tokenizer:** Custom BPE trained on TinyStories
- **Architecture:** 
  - 6 transformer layers
  - 384 embedding dimensions
  - 6 attention heads
  - 1536 FFN dimensions

## Usage
```python
from transformers import pipeline

# Load the model
generator = pipeline("text-generation", model="endurasolution/ronmicro-llm-story")

# Generate a story
story = generator(
    "Once upon a time",
    max_new_tokens=150,
    temperature=0.7,
    repetition_penalty=1.3,
    no_repeat_ngram_size=3,
    do_sample=True
)

print(story[0]["generated_text"])
```

## Example Outputs

**Prompt:** "Once upon a time"
**Output:** "Once upon a time, there was a little boy named Timmy. He loved to play with his toy cars and trucks all day long..."

## Limitations

- Trained on only 5% of TinyStories (Phase 1)
- May generate repetitive text occasionally
- Best for short children's stories (100-200 words)
- Limited to simple vocabulary and grammar

## Next Steps

Phase 2 training in progress with 20% data and 5 epochs for improved quality.

## Citation

Built using TinyStories dataset:
```
@article{eldan2023tinystories,
  title={TinyStories: How Small Can Language Models Be and Still Speak Coherent English?},
  author={Eldan, Ronen and Li, Yuanzhi},
  journal={arXiv preprint arXiv:2305.07759},
  year={2023}
}
```

## License

MIT License