wpferrell/mistral-7b-instruct-bigsmall

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 — lossless neural network weight compression. Every weight is bit-identical to the original. No accuracy loss whatsoever.

Install

pip install bigsmall

Load and run inference (streaming — under 2GB peak RAM)

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

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	67%	83%	None	CPU
DFloat11	~70%	BF16 only	~2x at batch=1	CUDA only
ZipServ	~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
• PyPI: pip install bigsmall