---
library_name: transformers
base_model:
- openai-community/gpt2
---
# Growing LLM Model Card

## Model Description

The **Growing LLM** is a GPT-2 based language model that implements neural plasticity-inspired dynamic growth during training. This model starts with a pre-trained GPT-2 (124M parameters) and dynamically adds new transformer blocks while freezing the original parameters, allowing the model to acquire new knowledge without catastrophic forgetting.

### Key Features

- **Dynamic Growth**: Adds new transformer blocks during training
- **Knowledge Preservation**: Freezes original parameters to retain pre-trained knowledge
- **Flexible Triggers**: Supports fixed schedule and plateau detection growth triggers
- **Regularization Options**: Supports Knowledge Distillation and Elastic Weight Consolidation (EWC)
- **Comprehensive Metrics**: Tracks training, validation, growth events, and scaling analysis

## Training Details

### Training Data
- Dataset: WikiText-2-raw-v1
- Max sequence length: 128 tokens

### Training Configuration
- Base model: GPT-2 (124M parameters)
- Learning rate: 5e-5
- Batch size: 8
- Optimizer: AdamW with weight decay 0.01
- Max steps: 2000
- Growth frequency: Every 500 steps
- Maximum growth events: 3

### Growth Mechanism
1. **Fixed Schedule**: Grow every N training steps
2. **Plateau Detection**: Grow when validation loss shows no improvement for Y steps

### Regularization (Optional)
- **Knowledge Distillation**: Uses teacher-student architecture with temperature scaling
- **Elastic Weight Consolidation (EWC)**: Penalizes changes to important parameters

## Model Architecture

- Base: GPT-2 (12 layers, 12 heads, 768 hidden dim)
- Growth: Added 3 new transformer blocks (one per growth event)
- Final: 15 layers, 145.7M total parameters

## Training Results

### Summary Metrics
| Metric | Initial | Final |
|--------|---------|-------|
| Training Loss | 7.16 | 1.95 |
| Validation Loss | 6.99 | 2.03 |
| Validation Perplexity | ~1000 | 7.58 |
| Total Parameters | 124.4M | 145.7M |

### Training Time
- Total time: ~60 minutes (3596 seconds)
- Best validation loss: 2.00
- Best validation perplexity: 7.42

### Growth Events
| Growth # | Step | Layers | Parameters Added | Val Loss Delta |
|---------|------|--------|-----------------|----------------|
| 1 | 500 | 12 → 13 | +7.1M | +0.00003 |
| 2 | 1000 | 13 → 14 | +7.1M | +0.00002 |
| 3 | 1500 | 14 → 15 | +7.1M | +0.000001 |


### RESULTS SUMMARY

| Model | Perplexity | Loss |
|-------|------------|------|
| Base GPT-2 | 56.39 | 4.0323 |
| Growing LLM | 33.39 | 3.5082 |

Perplexity improvement: 40.8%

**Key Observation**: The validation loss delta after each growth event is minimal (~0.00003), demonstrating successful knowledge retention. The model continues to learn new capabilities without catastrophic forgetting.

## Usage

```python
from transformers import GPT2LMHeadModel, AutoTokenizer

# Load model and tokenizer
model = GPT2LMHeadModel.from_pretrained("aicinema69/gpt2-growing")
tokenizer = AutoTokenizer.from_pretrained("aicinema69/gpt2-growing")

# Generate text
input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))
```

## Limitations

- Growth events may cause temporary performance dips that recover with continued training
- Requires sufficient training data to benefit from additional parameters
- More parameters = higher memory and compute requirements

## License

This model is based on GPT-2 which has the [OpenAI GPT-2 License](https://github.com/openai/gpt-2/blob/master/LICENSE).

## Citation

If you use this model in your research, please cite:

```bibtex
@misc{growing_llm,
  author = {Satyam Singh},
  title = {Growing LLM: Dynamic Model Growth for Continual Learning},
  year = {2026},
  publisher = {HuggingFace},
  howpublished = {\url{https://huggingface.co/aicinema69/gpt2-growing}}
}
```

## Contact

For questions or issues, please open a GitHub issue or contact the model author.