| --- |
| 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` |
|
|
