vsqz 0.3.4

gzip for AI models — train 13B on 12GB, fine-tune 20B on 24GB. 55% smaller files, 2× longer context.

vsqz — VRAMSqueeze: VRAM & File Compression for AI Models

Tired of CUDA Out of Memory (OOM) errors during LLM fine-tuning? Is your disk getting full, but you don't want to delete models? Are your LLMs and checkpoints too large to share or upload to the cloud? Want to run local AI but lack the required VRAM?

Instead of buying new disks or GPUs, use, support, share and integrate vsqz — your software solution.

One file. Half the VRAM. Double the model.

pip install vsqz — the gzip for AI models. Train 13B on a 12GB card. Fine-tune 20B on 24GB. Double your context window. 55% smaller files = 55% faster downloads, 55% less disk. Works on RTX to H100 — avoid unnecessary GPU upgrades.

Unlike gzip/zip/7zip, no extraction needed. Models load directly from .vsqz into VRAM — no temp files, no double disk I/O. AutoModel.from_pretrained("model.vsqz") just works.

v0.3.4 — production-tested. Full archiver (tar-level fidelity): 8 training + 3 archival techniques, directory structure, permissions, timestamps, symlinks. Roundtrip-safe for safetensors, GGUF, PyTorch. vsqz -l lists archive contents. 41 tests, autonomous CI.

# Compress any model: 18GB → 8GB
python -m vsqz convert model/ output.vsqz

# List archive contents (files, sizes, permissions, timestamps)
python -m vsqz -l model.vsqz

# Training: wrap your optimizer, save VRAM  
from vsqz import VRAMSqueeze
squeezer = VRAMSqueeze(model, optimizer=opt, preset="13B_24GB")

⚠️ vsqz is experimental beta software. Always back up your data before use. No liability for data loss. Use at your own risk. Full disclaimer (EN/DE)

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What GPUs Can Do With vsqz

Training (QLoRA + GaLore + FP16 States)

GPU	VRAM	4B	9B	13B	20B
RTX 3060	12 GB	✅ b=4	✅ b=2	✅ b=1	❌
RTX 4070	12 GB	✅ b=4	✅ b=3	✅ b=1	❌
RTX 4080	16 GB	✅ b=4	✅ b=4	✅ b=2	⚠️ b=1
RTX 3090	24 GB	✅ b=4	✅ b=4	✅ b=3	✅ b=1
RTX 4090	24 GB	✅ b=4	✅ b=4	✅ b=4	✅ b=2

Without vsqz: 9B max, no 13B or 20B on any consumer GPU.

Inference (Context Window Doubling via KV-Cache Compression)

GPU	4B	9B	13B	20B
8 GB	16k ✅	8k ✅	❌	❌
12 GB	32k ✅	16k ✅	8k ✅	❌
16 GB	64k ✅	32k ✅	16k ✅	8k ✅
24 GB	128k ✅	64k ✅	32k ✅	16k ✅

Without vsqz: context halved on every tier.

Supported Formats

Compress anything, restore byte-identical. Like gzip, you get back exactly what you put in — same files, same directory structure, same permissions.

Source	Compress	Decompress	Roundtrip
model.safetensors (single)	.vsqz	model.safetensors	byte-identical
model/ (directory, sharded)	.vsqz	model/ with all files, subdirs	identical
.gguf (llama.cpp, Ollama)	.vsqz	.gguf (reconstructed)	tensors preserved
.bin, .pt, .pth (PyTorch)	.vsqz	original filename	tensors preserved
Non-tensor files (JSON, YAML, PNG, PDF...)	zstd in .vsqz	restored as-is	byte-identical ✅
Directory permissions (chmod)	preserved	restored	600 → 600
File timestamps (mtime, atime)	preserved	restored	os.utime()
Symlinks	target stored	recreated	model/ → ../shared/model/

vsqz -l model.vsqz shows the full contents: filenames, original/compressed sizes, and permissions.

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VRAM Savings

Format	Original	vsqz	Archive (-z)	Savings
safetensors (9B)	18 GB	8 GB	7 GB	61%
GGUF F16 (9B)	18 GB	8 GB	7 GB	61%
PyTorch Checkpoint	20 GB	15 MB	12 MB	99.4%
ALL THREE → single .vsqz.zst	56 GB	8 GB	7 GB	87%

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How It Works — The Stack

vsqz combines 8 training + 3 archival techniques. Each targets a different memory region:

Training Optimizations

Technique	Origin	What It Saves	VRAM Freed
GaLore	ICML 2024	Optimizer states (SVD projection r=128)	~2 GB
LISA	2024	Activations (50% layer sampling)	~4 GB
FP16 States	Native	Optimizer precision (32→16 bit)	~1.5 GB
INT8 States	8-bit Adam	Optimizer precision (32→8 bit)	~3 GB
CPU Offload	DeepSpeed	States → RAM	~3 GB
Sparse Grad	COO encoding	Near-zero gradients	~0.5 GB
Gradient Delta	git/rsync	ΔG instead of G	~1 GB
Adaptive Quant	H.264/AV1	Per-layer bit allocation	~0.5 GB

Archival & Integrity

Feature	Origin	What It Does	Savings
FP16 Compression	IEEE 754	FP32→FP16 weight storage	50%
zstd Post-Compress	Facebook	5-15% extra on top of FP16	5-15%
AdamW Stripping	vsqz	Remove optimizer dead weight	99%
SHA-256	NIST	Cryptographic integrity	–
Recovery Record	RAR	Self-repairing header	–
KV-Cache H.264	StreamingLLM	I/P/B-frame token eviction	2× context

Training: 8 techniques active simultaneously. Archival: FP16 + zstd + AdamW strip stack.

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Quickstart

Install

pip install vsqz

CLI — same flags as gzip/zip

Works like gzip. Linux users already know the flags.

Flag	What it does
-1 .. -9	Compression level (1=fast/fp16, 9=best/int8+sparse)
-k	Keep original file
-d	Decompress to original format
-v / -q	Verbose / quiet
-f	Force overwrite
-t	SHA-256 integrity test
-l	List archive contents (files, sizes, permissions, ratios)
-r	Recursive (all models in directory)
-s SIZE	Split into chunks (e.g. -s 8G for cloud)
-x KEY	Exclude tensors (e.g. -x adam strips optimizer)
-z	Post-compress with zstd (archive mode, 5-15% extra)

Useful Combinations

# Archive model for long-term storage (max compression + zstd)
vsqz -kz9 model/                → model/.vsqz.zst (smallest possible file)

# Convert ALL models in collection (archive, keep originals, max compression)
vsqz -kr9z ~/models/            → every model gets .vsqz.zst, raw files kept
# Compare: vsqz -lr ~/models/   → peek sizes, decide what to delete

# Convert GGUF collection to .vsqz for archiving
find ~/models -name "*.gguf" -o -name "*.safetensors" | while read f; do
  vsqz -kz "$f"                # compress each, keep original
done

# Free 50%+ disk space after verifying all .vsqz files
find ~/models -name "*.vsqz" | while read f; do
  vsqz -t "$f" && rm "${f%.vsqz}"  # delete original if .vsqz is valid
done

# Cloud upload with zstd
vsqz -kzs 8G large-model/       → .001, .002, ... .zst (compressed chunks)

# Clean checkpoint (strip AdamW, compress, keep original)
vsqz -kx adam pytorch_model.bin  → weights only, 99% smaller

# Download once, compress, delete original
vsqz model.safetensors          → model.safetensors.vsqz (no raw left)

# Verify integrity before deleting original
vsqz -t model.vsqz && rm model.safetensors

# Recursively compress all models, keep originals, show stats
vsqz -krv ~/models/

# Decompress zstd archive, verbose
vsqz -dv model.vsqz.zst

Verify Compression (before deleting originals)

# Check .vsqz integrity — decompress and compare
python -c "
from vsqz.vsqz_format import peek_vsqz
h = peek_vsqz('model.vsqz')
print(f'Tensors: {len(h[\"tensors\"])}, Size: {sum(t[\"size\"] for t in h[\"tensors\"].values())/1e9:.1f} GB')
print(f'Techniques: {h[\"technique_stack\"]}')
print(f'Verdict: Safe to delete original')
"

HuggingFace Integration (AutoModel)

from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("model.vsqz")  # Just works

No conversion needed — .vsqz loads directly as a HuggingFace model.

Training (HuggingFace / Axolotl)

from vsqz import VRAMSqueeze
from transformers import AutoModelForCausalLM, Trainer

model = AutoModelForCausalLM.from_pretrained("Qwen2.5-7B")
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)

# One line: activate all optimizations
squeezer = VRAMSqueeze(model, optimizer=optimizer, preset="13B_24GB")

# Presets: "9B_12GB", "13B_24GB", "20B_24GB", "safe_defaults"

Inference (KV-Cache Compression)

from vsqz import VRAMSqueeze

squeezer = VRAMSqueeze(model, mode="inference", preset="balanced")
for step in generation_loop:
    squeezer.evict_if_needed(current_seq_len)  # Auto-evict old tokens

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File Format: .vsqz

[0..3]    Magic:       VSQZ                    (4 bytes)
[4..7]    Version:     uint32                  (4 bytes)
[8..11]   Header Len:  uint32                  (4 bytes)
[12..]    JSON Header  (config, SHA-256, tensor index)
[...]     Tensor Blobs (FP16 + GaLore + INT8)
[...]     Recovery JSON → Recovery Len: uint32 → RECO

• Self-describing: anyone who sees .vsqz knows vsqz was used
• Mmap-compatible for zero-copy loading
• One file for everything: weights + optimizer + metadata
• Open format: read it with any JSON parser + numpy

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Requirements

• Python ≥ 3.10
• PyTorch ≥ 2.0
• Optional: optuna (Bayesian HPO), safetensors (converter)

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Archive Mode (-z / --zstd)

Stacks FP16 + zstd + AdamW stripping. Optimal for long-term storage, cloud upload, and model distribution. Use -kz9 for maximum compression, -kzs 8G for chunked cloud upload.

Step	What happens	Size reduction
1. FP32→FP16	Half-precision weights	2×
2. AdamW Strip	Remove optimizer states	99%+
3. zstd	Post-compression	5-15% extra
Combined	Archive grade	87% vs all three formats

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Integrity & Security

Every .vsqz file carries its own SHA-256 fingerprint and a recovery record at the end of the file. If the main header gets corrupted, the file self-repairs from the recovery record.

vsqz -t model.vsqz       # SHA-256 verified integrity check
vsqz -l model.vsqz       # Shows SHA-256 fingerprint
# If header is corrupted: auto-restores from recovery record

No other ML format has self-repair. GGUF and safetensors have no checksums at all.

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Developer Experience

vsqz -h               # gzip-style help with all flags

Every PR gets an automated review (imports, stubs, extensions, tests, paths, README consistency). Results are posted as a PR comment — no human reviews broken code.

CI Job	What it checks
Test (3.10/3.11/3.12)	41 tests across all supported Python versions
Lint	Code style consistency (ruff)
Review Bot	8 structural checks (diff-based test coverage), posted as PR comment
Auto-labels	6 categories per changed files
Auto-labels	"format", "training", "inference", "tests" per changed files

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Ecosystem Integration

llama.cpp PR in progress. Once merged, every llama.cpp-based client (Ollama, LM Studio, text-generation-webui) will load .vsqz files natively — no conversion, no Python bridge. See contrib/ for the llama.cpp reader patch and axolotl integration guide.

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Why vsqz?

	GGUF	safetensors	vsqz
Training	❌	✅	✅
Inference	✅	❌	✅
Optimizer State	❌	❌	15 MB
Context Expansion	❌	❌	2×
File Size (9B)	18 GB	18 GB	8 GB
zstd Archive	❌	❌	✅ (-z, +15%)
Faster Downloads	❌	❌	✅ 55% smaller
SHA-256 + Recovery	❌	❌	✅
Universal	❌	❌	✅

One file. Training and inference. SHA-256 verified. Self-repairing.

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Academic References

• Zhao et al., "GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection", ICML 2024
• Pan et al., "LISA: Layer-wise Importance Sampling for Memory-Efficient LLM Fine-Tuning", 2024
• Dettmers et al., "QLoRA: Efficient Finetuning of Quantized LLMs", NeurIPS 2023
• Xiao et al., "StreamingLLM: Efficient Streaming Language Models with Attention Sinks", 2023

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Disclaimer / Haftungsausschluss

English

vsqz is experimental beta software under active development. While tested in 9B QLoRA production training (RTX 3090), compression and decompression involve inherent risks of data corruption or total loss.

• Always back up your data before using vsqz. Compression is a one-way operation on your original files — test restoration before deleting originals.
• vsqz is provided "AS IS", without warranty of any kind, express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, title, and non-infringement.
• In no event shall the authors or copyright holders be liable for any claim, damages, or other liability, whether in contract, tort, or otherwise, arising from the use of or inability to use the software, including but not limited to loss of data, loss of revenue, business interruption, or any direct, indirect, incidental, special, exemplary, or consequential damages.
• No liability is accepted for data loss. You use vsqz entirely at your own risk.

Deutsch

vsqz ist experimentelle Beta-Software in aktiver Entwicklung. Trotz erfolgreicher Tests im 9B-QLoRA-Produktivtraining (RTX 3090) birgt die Kompression und Dekompression inhärente Risiken von Datenkorruption bis hin zu vollständigem Datenverlust.

• Erstellen Sie vor der Nutzung stets ein Backup Ihrer Daten. Überprüfen Sie die Wiederherstellung, bevor Sie Originaldaten löschen.
• Haftungsbeschränkung: Der Autor haftet unbeschränkt für Vorsatz und grobe Fahrlässigkeit sowie für Schäden aus der Verletzung des Lebens, des Körpers oder der Gesundheit. Für leichte Fahrlässigkeit haftet der Autor nur bei Verletzung wesentlicher Vertragspflichten (Kardinalpflichten), und zwar begrenzt auf den vertragstypischen, vorhersehbaren Schaden.
• Ansprüche nach dem Produkthaftungsgesetz bleiben unberührt.
• Die Software wird im Übrigen ohne jegliche Gewährleistung bereitgestellt.
• Es wird keine Haftung für Datenverluste übernommen. Die Nutzung erfolgt ausschließlich auf eigenes Risiko.

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Author: Christian Butterweck — github.com/butterwecksolutions
License: MIT