vsqz 0.2.7

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

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. Save 55% disk & webspace. Works on RTX to H100 — avoid unnecessary GPU upgrades.

v0.2.6 — production-tested. 8 training + 3 archival techniques verified in 9B QLoRA (RTX 3090). 41 tests, autonomous CI, PR review bot. AutoModel.from_pretrained(".vsqz") works. Test on your setup before relying on it for critical workloads. PRs welcome.

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

# Info: peek without loading
python -m vsqz info model.vsqz

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

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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.

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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 metadata (shows fingerprint)
-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%)
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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Author: Christian Butterweck — github.com/butterwecksolutions
License: MIT