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

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

@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
• 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.