File size: 2,685 Bytes
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license: apache-2.0
tags:
- bigsmall
- compression
- lossless
- qwen2
---
# Qwen 2.5 14B Instruct (BigSmall compressed)
**29.5 GB → 19.5 GB (66.1%). Under 2 GB peak RAM. Full quality — not quantization.**
This is Qwen2.5-14B-Instruct compressed with [BigSmall](https://github.com/wpferrell/Bigsmall) — lossless neural network weight compression. Every weight is bit-identical to the original. No accuracy loss whatsoever.
## Install
```bash
pip install bigsmall
```
## Load and run inference (streaming — under 2GB peak RAM)
```python
from bigsmall import StreamingLoader
from transformers import AutoModelForCausalLM, AutoTokenizer
loader = StreamingLoader("wpferrell/qwen2.5-14b-instruct-bigsmall")
model = loader.load_model(AutoModelForCausalLM)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
messages = [{"role": "user", "content": "Explain lossless compression."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```
## Or use AutoModel with the transparent hook
```python
import bigsmall
bigsmall.install_hook()
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("wpferrell/qwen2.5-14b-instruct-bigsmall")
```
## Compression stats
| Metric | Value |
|--------|-------|
| Original size | 29.5 GB |
| Compressed size | 19.5 GB |
| Ratio | 66.1% (BF16) |
| Format | BF16 → BigSmall (.bs shards) |
| Lossless verified | md5 every tensor |
| Peak RAM (streaming) | < 2 GB |
## 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** |
## 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`
- Paper: [BigSmall: Lossless Neural Network Weight Compression at the Joint Entropy Floor](https://github.com/wpferrell/Bigsmall/blob/main/paper.pdf)
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