bigsmall 3.14.4

Lossless AI model compression — make any model 34% smaller with bit-identical weights, drop-in replacement for HuggingFace from_pretrained

BigSmall — Lossless AI Model Compression

Make any AI model ~34% smaller. Bit-identical weights. Drop-in replacement for from_pretrained.

pip install bigsmall          # CLI + compression/decompression
pip install bigsmall[torch]   # add this for model loading (from_pretrained)

A 14 GB Mistral-7B becomes 9.3 GB. A fine-tuned model becomes a 5 GB patch on top of its 14 GB base. The decompressed model is every weight bit-for-bit identical to the original — each tensor's md5 is verified on decompress. (Verification is tensor-level, not file-level: safetensors re-serializes the container wrapper, so the file's md5 changes, but every weight value is bit-for-bit identical.)

~34% smaller	~65% smaller as a delta patch	25+ ready-to-use models
any BF16 LLM	fine-tunes vs their base	on HuggingFace

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What BigSmall does

Three use cases. Pick the one that fits.

1. Make any model smaller

bigsmall compress mistral-7b/ -o mistral-7b.bs
bigsmall decompress mistral-7b.bs -o mistral-7b-restored/

Before: 14.2 GB of safetensors. After: 9.3 GB .bs file. Saved: 4.9 GB (34%).

Every weight is bit-for-bit identical. Every calculation the model does is identical to the original. Works on any safetensors model — LLMs, diffusion, audio, vision, anything.

2. Store fine-tunes as tiny patches

bigsmall compress qwen-instruct/ --delta-from qwen-base/ -o instruct.bs
bigsmall apply qwen-base/ instruct.bs -o qwen-instruct-restored/

Before: 14.2 GB Qwen2.5-7B-Instruct. After: ~5 GB patch. Saved: 9 GB (65%).

If your users already have the public base model, they only need to download what changed. This is the biggest win in BigSmall. Use it for any fine-tune: instruction tuning, DPO, RLHF, domain adaptation, LoRA-merged checkpoints.

3. Download smaller, use instantly

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "wpferrell/phi-3.5-mini-instruct-bigsmall"
)

Works exactly like a normal HuggingFace model — BigSmall decompresses transparently on load. 25+ pre-compressed models ready to use (browse them all).

Prefer the CLI? bigsmall decompress works on local .bs files — download first, then decompress:

hf download wpferrell/phi-3.5-mini-instruct-bigsmall --local-dir phi-3.5-mini-bs
bigsmall decompress phi-3.5-mini-bs/model-00001-of-00002.bs -o model.safetensors

(On older huggingface_hub the equivalent command is huggingface-cli download …; the huggingface-cli entrypoint is deprecated in huggingface_hub >= 1.0 in favour of hf.)

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Compression numbers (every published model)

Every row is a real measurement. Click a model to download it.

Model	Original	BigSmall	Saved
Qwen2.5-14B-Instruct	29.5 GB	19.5 GB	34%
Gemma-3-12B-it	22.7 GB	14.8 GB	35%
Gemma-2-9B-it	17.2 GB	11.3 GB	34%
Llama-3.1-8B-Instruct	15.0 GB	9.7 GB	35%
Llama-3-8B-Instruct	15.0 GB	9.8 GB	34%
Qwen3-8B	15.3 GB	10.1 GB	34%
Mistral-7B-Instruct v0.3	14.2 GB	8.9 GB	37%
Mistral-7B-Instruct v0.2	14.2 GB	8.9 GB	37%
Qwen2.5-7B-Instruct	14.2 GB	9.4 GB	34%
Phi-3.5-mini-instruct	7.1 GB	4.7 GB	34%
Gemma-3-4B-it	8.0 GB	5.2 GB	35%
Qwen3-4B-Instruct	7.5 GB	5.0 GB	34%
Llama-3.2-3B-Instruct	6.4 GB	3.9 GB	39%
Gemma-2-2B-it	4.9 GB	3.2 GB	34%
Qwen2.5-3B-Instruct	5.7 GB	3.8 GB	34%
Qwen2.5-1.5B-Instruct	2.9 GB	1.9 GB	34%
Llama-3.2-1B-Instruct	2.3 GB	1.5 GB	34%
Gemma-3-1B-it	1.9 GB	1.2 GB	35%
Qwen2.5-0.5B-Instruct	920 MB	610 MB	34%
GPT-2 (117M)	548 MB	414 MB	24%
Gemma-3-270M-it	500 MB	330 MB	34%
Gemma-3-270M	500 MB	330 MB	34%
Gemma-2-2B	9.7 GB	8.1 GB	17%

Browse all 25+ models on HuggingFace →

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What "lossless" actually means

Every weight in the model is mathematically identical to the original — same bit pattern, same floating-point value, same gradient, same output.

• Not quantization. Quantization rounds weights to fewer bits and the model's behaviour changes.
• Not pruning. Pruning deletes weights.
• Not approximation. No tricks, no calibration data, no quality drop.

BigSmall finds redundancy in the bit pattern of neural weights and stores it more compactly — the same idea as ZIP for text, but tuned for BF16 floating-point distributions. md5 is verified on every tensor at decompression. If a single bit differs, verify fails.

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How it compares

Approach	Lossless?	Typical reduction	Behaviour change
BigSmall	Yes — bit-identical	~34% (65% as a delta)	None
Quantization (GPTQ / AWQ / bitsandbytes)	No	50–75%	Yes — weights are rounded
DFloat11 (BF16→FP11)	No	~31% (fixed)	Yes — mantissa truncated
ZipNN	No	~20–30%	Yes — quality loss
ZIP / gzip on safetensors	Yes	~1–3%	None (but not model-aware)

BigSmall is the only option here that is both lossless and meaningfully smaller: every weight, gradient, and output is identical to the original, so it's a drop-in for any workflow without re-evaluation. Quantization compresses further but changes the model; generic ZIP keeps fidelity but barely shrinks BF16 weights. See docs/comparison.md for the full breakdown.

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CLI reference

bigsmall compress SRC [-o OUT] [--delta-from BASE] [--auto-delta] [--resume] [--ecc]
bigsmall decompress SRC [-o OUT] [--base BASE]
bigsmall info SRC.bs                       size, ratio, codecs used
bigsmall scan SRC                          analyse before compressing
bigsmall verify SRC.bs [--fast|--sample N] integrity check
bigsmall diff A.bs B.bs [--patch P.bs]     compare or write a delta
bigsmall apply BASE PATCH.bs -o OUT        reconstruct from base + patch
bigsmall repair SRC.bs [-o OUT]            recover via Reed-Solomon ECC sidecar
bigsmall benchmark SRC                     encode/decode throughput
bigsmall migrate SRC.bs                    re-encode with current codecs
bigsmall status                            list your BigSmall HF repos
bigsmall pipeline run SRC DST              resumable download → compress → upload
bigsmall reshard SRC --output-dir DIR [--size-gb N|--shards N|--join]  reshard .bs by layer

Every command has --help. See docs/cli-reference.md for full examples.

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Python API

import bigsmall

# Round-trip a model
bigsmall.compress("model/", "model.bs")
bigsmall.decompress("model.bs", "model_back/")

# Fine-tune as a delta patch
bigsmall.compress("finetune/", "patch.bs", delta_from="base/")
bigsmall.apply("base/", "patch.bs", "finetune_back/")

# Inspect before compressing
bigsmall.detect_bf16_native("model/")
bigsmall.scan_model("model/")

# Low-VRAM streaming inference (~12× less VRAM than from_pretrained)
from bigsmall import BigSmallStreamingModel
model = BigSmallStreamingModel.from_pretrained(
    "wpferrell/phi-3.5-mini-instruct-bigsmall",
    device="cuda",
    lru_max_vram_gb=2.0,
)

# Stream-decompress straight from the HF CDN — no .bs written to disk (V10)
state_dict = bigsmall.stream_from_hub("wpferrell/gpt2-bigsmall", device="cpu")

# Reshard .bs files along layer boundaries, no re-encoding (V11)
bigsmall.reshard(["model.bs"], "resharded/", target_shard_size_gb=2.0)

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What's new in v3.14

• GPU-resident KV cache (V9) — GPUCompressedKVCache keeps the compressed cache and the encode/decode passes entirely on the CUDA device, with no CPU round-trip. ~47× faster than the CPU KV codec on the reference shape, bit-identical round-trip. get_kv_cache(device, mode) auto-picks the GPU backend when CUDA is available. V9B adds fused Triton pack/unpack kernels.
• Progressive HTTP streaming (V10) — stream_from_hub(repo_id) decompresses a model directly from the HuggingFace CDN over HTTP byte-range requests. With the default cache=False, zero .bs bytes are written to disk.
• Reshard (V11) — bigsmall reshard splits, joins, or rebalances .bs shards along transformer-layer boundaries with no re-encoding. Every output tensor is md5-verified.
• numba is now a hard dependency (numba>=0.61) — guarantees the JIT codec path runs everywhere instead of a silent slow fallback.
• CI green across the full matrix — Ubuntu / Windows / macOS × Python 3.10 / 3.11 / 3.12.

Earlier highlights still current: delta compression (fine-tunes as ~34%-size patches), --auto-delta base detection, BF16-native F32 auto-routing (Whisper-class), --resume, verify --fast/--sample, mmap decode, Reed-Solomon --ecc + repair, and BigSmallStreamingModel(lru_max_vram_gb=…).

Full changelog →

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Research

The lossless compression ceiling for BF16 neural weights has been measured. It is ~62% of raw BF16 for any model, ~34% for fine-tunes with delta compression. We ran 300+ experiments across every known mathematical approach — entropy coding, cross-tensor prediction, learned translators, persistent homology, optimal transport, quantum-inspired methods, and more — and proved that there is no further compression available within the strict bit-identity contract.

Full findings, all experiments, all dead-ends: 10.5281/zenodo.20279247. Plain-English summary: docs/research.md.

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Install

pip install bigsmall                  # core
pip install "bigsmall[hf]"            # + HuggingFace integration
pip install "bigsmall[ecc]"           # + Reed-Solomon error recovery
pip install "bigsmall[all]"           # everything

Requires Python 3.9+. Works on Linux, macOS, and Windows. CPU, NVIDIA, AMD, and Apple Silicon.

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License

Code: Elastic License 2.0. Free for personal, research, and commercial use. SaaS providers should see LICENSING.md.

Model weights distributed in .bs format keep the license of the original model.

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Links

• PyPI — https://pypi.org/project/bigsmall/
• GitHub — https://github.com/wpferrell/Bigsmall
• HuggingFace — https://huggingface.co/wpferrell
• Paper / DOI — https://doi.org/10.5281/zenodo.20279247 (always resolves to the latest version)
• Paper (PDF) — https://github.com/wpferrell/Bigsmall/blob/main/paper.pdf
• Docs — docs/
• Changelog — CHANGELOG.md
• Contact — [e-mail]