---
license: apache-2.0
tags:
  - bigsmall
  - compression
  - lossless
  - gpt2
---

# GPT-2 (BigSmall compressed)

**548MB → 414MB (75.5%). Bit-identical. Under 500MB peak RAM with streaming.**

This is GPT-2 117M compressed with [BigSmall](https://github.com/wpferrell/Bigsmall) — lossless neural network weight compression. Not quantization. Every weight is bit-identical to the original.

## Install

```bash
pip install bigsmall
```

## Load and run inference (streaming)

```python
from bigsmall import StreamingLoader
from transformers import GPT2LMHeadModel, GPT2Tokenizer

# Streams one layer at a time — under 500MB peak RAM
loader = StreamingLoader("wpferrell/gpt2-bigsmall")
model = loader.load_model(GPT2LMHeadModel)
tokenizer = GPT2Tokenizer.from_pretrained("openai-community/gpt2")

inputs = tokenizer("Hello, I'm a language model", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))
```

## Or decompress to disk first

```python
from bigsmall import from_pretrained
from transformers import GPT2LMHeadModel
model = from_pretrained("wpferrell/gpt2-bigsmall", model_class=GPT2LMHeadModel)
```

## What's inside

| File | Original | Compressed | Ratio |
|------|----------|------------|-------|
| model.safetensors (FP32) | 548 MB | 414 MB | 75.5% |

Verified lossless: md5 of every weight tensor matches original after decompression.

## Comparison

| Tool | BF16 Ratio | FP32 Ratio | Inference Overhead | Hardware |
|------|------------|------------|-------------------|---------|
| [ZipNN](https://arxiv.org/abs/2411.05239) | 67% | 83% | None | CPU |
| [DFloat11](https://arxiv.org/abs/2504.11651) | ~70% | BF16 only | ~2x at batch=1 | CUDA only |
| [ZipServ](https://arxiv.org/abs/2603.17435) | ~70% | BF16 only | 1.22x faster | GDDR GPU |
| **BigSmall** | **65.6%** | **75.5%** | **None** | **CPU + any GPU** |

*Lower ratio = better compression. BigSmall BF16 measured on Mistral 7B.*

## About BigSmall

BigSmall compresses at the joint entropy floor for neural network weights. It codes sign+exponent jointly and mantissa conditioned on exponent, achieving the information-theoretic minimum. The streaming loader decompresses one transformer layer at a time directly into VRAM.

- GitHub: [wpferrell/Bigsmall](https://github.com/wpferrell/Bigsmall)
- PyPI: `pip install bigsmall`