README.md

# GPT-2 XL Compressed Model Weights

This dataset contains the compressed model weights from **tensor network compression** methodology applied to GPT-2 XL.

## 📁 Files Included

### Compressed Model Weights (.pt files)
- `compressed_gpt2_xl_68.3%.pt` - Base compressed model (~68% compression)
- `compressed_gpt2_xl_68.3%_healed.pt` - Compressed + knowledge distillation healing
- `compressed_gpt2_xl_68.3%_enwik8_trained.pt` - Compressed + enwik8 fine-tuning
- `compressed_gpt2_xl_68.3%_enwik8_final.pt` - Final version after training
- `compressed_gpt2_xl_68.3%_enwik8_finetuned.pt` - Fine-tuned version

### Architecture & Metadata
- `model_architecture.pkl` - Compressed model architecture
- `*_metadata.json` - Training and compression metadata

## 🔬 Methodology

Based on quantum-inspired tensor network compression:
- **Matrix Product Operator (MPO)** tensor network decomposition
- **68% parameter reduction** (1.56B → ~500M parameters)  
- **Tensor network** compression technique
- **Knowledge distillation** healing process

## 🚀 Usage

```python
import torch

# Load compressed weights
model_weights = torch.load('compressed_gpt2_xl_68.3%_healed.pt', map_location='cpu')

# For ready-to-use model, see: 
# https://huggingface.co/prompterminal/gpt2-compressed
```

## 📊 Compression Stats

- **Original GPT-2 XL**: 1.56B parameters, ~6.2GB
- **Compressed Version**: ~500M parameters, ~1.98GB  
- **Compression Ratio**: 68% reduction
- **Method**: MPO tensor networks + healing

## 🎯 Files Recommended for Use

- **Best for inference**: `compressed_gpt2_xl_68.3%_healed.pt`
- **Best for fine-tuning**: `compressed_gpt2_xl_68.3%_enwik8_trained.pt`
- **Research/analysis**: All files + metadata

## 📚 Citation

```bibtex
@misc{tensor_network_compression_2024,
  title={GPT-2 XL Compressed using Tensor Network Methods},
  author={prompterminal},
  year={2024},
  howpublished={HuggingFace Dataset}
}
```

## 🔗 Related

- **Ready-to-use model**: [prompterminal/gpt2-compressed](https://huggingface.co/prompterminal/gpt2-compressed)
- **Tensor network compression research**: Matrix Product Operator methods

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*These weights represent pioneering work in tensor network compression for large language models.*