README.md

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

# Mistral 7B Instruct (BigSmall compressed)

**14 GB → 9.3 GB. Under 2 GB peak RAM. Full quality — not quantization.**

This is Mistral-7B-Instruct-v0.3 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 MistralForCausalLM, AutoTokenizer

# Streams one layer at a time — 9.3GB download, under 2GB peak RAM
loader = StreamingLoader("wpferrell/mistral-7b-instruct-bigsmall")
model = loader.load_model(MistralForCausalLM)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")

messages = [{"role": "user", "content": "Explain lossless compression in one paragraph."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))
```

## Or decompress to disk first

```python
from bigsmall import from_pretrained
from transformers import MistralForCausalLM
model = from_pretrained("wpferrell/mistral-7b-instruct-bigsmall", model_class=MistralForCausalLM)
```

## Compression stats

| Metric | Value |
|--------|-------|
| Original size | 14.2 GB |
| Compressed size | 9.3 GB |
| Ratio | 65.6% (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** |

*Lower ratio = better compression.*

## 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 — making 7B+ models accessible on hardware that couldn't otherwise load them.

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