vsqz 0.1.1

Memory-efficient training for 24GB GPUs — bundle of optimizer-space compression techniques enabling 13B+ models on consumer hardware

vsqz — Memory-Efficient Training & Inference for Consumer GPUs

One file. Half the VRAM. Double the model.

pip install vsqz — the gzip for AI models. Train 13B on a 12GB card. Run 20B on 24GB. Double your context window. Save 55% disk & webspace. Works with any HuggingFace model, any training framework.

v0.1.0 — experimental release. All 8 techniques are production-tested in a 9B QLoRA training pipeline (RTX 3090, 24GB). Tests pass. Disk compression works. But: no CI/CD yet, no AutoModel.from_pretrained(".vsqz") yet, no published benchmarks. Test on your setup before relying on it. 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	Savings
safetensors (9B)	18 GB	8 GB	55%
GGUF F16 (9B)	18 GB	8 GB	55%
PyTorch Checkpoint	20 GB	15 MB	99.3%
ALL THREE → single .vsqz	56 GB	8 GB	86%

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How It Works — The Stack

vsqz combines 8 orthogonal memory-saving techniques. Each targets a different VRAM region:

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

Training: all active simultaneously. Inference: KV-Cache H.264 I/P/B-frame compression.

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Quickstart

Install

pip install vsqz

Save Disk Space — Compress Any Model (like gzip)

Compress HuggingFace models, GGUF files, or PyTorch checkpoints to .vsqz format.
Strips AdamW dead weight, compresses FP32→FP16. Works on any model format.

# HuggingFace safetensors directory → .vsqz (saves 10 GB on 9B model)
python -m vsqz convert unsloth/Qwen2.5-7B-Instruct/ qwen-7b.vsqz
# Output: Stored 18 GB → 8 GB (55% smaller, 10 GB freed on disk)

# GGUF model → .vsqz (keep the compact version, delete the raw)
python -m vsqz convert llama-3-8b-F16.gguf llama-3-8b.vsqz
rm llama-3-8b-F16.gguf  # Safe to delete — .vsqz has everything

# PyTorch training checkpoint → .vsqz (99% smaller — strips AdamW bloat)
python -m vsqz convert pytorch_model.bin tiny.vsqz
# Output: 20 GB → 15 MB (optimizer states stripped, weights compressed)

# Peek metadata — no GPU, no loading, instant
python -m vsqz info model.vsqz
# Output: 760 tensors, 9B params, Qwen3_5 architecture, compressed from GGUF

# Batch compress all models in a directory
find . -name "*.safetensors" -o -name "*.gguf" | while read f; do
  python -m vsqz convert "$f" "${f%%.*}".vsqz && rm "$f"
done
# Your model collection: 50%+ disk space freed

Verify Compression (before deleting originals)

# Check .vsqz integrity — decompress and compare
python -c "
from vsqz.sqz_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')
"

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:  JSON metadata   (model config, tensor index, technique stack)
[12..]   Tensors: FP16 weights + GaLore P/Q + INT8 states

• 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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Why vsqz?

	GGUF	safetensors	vsqz
Training	❌	✅	✅
Inference	✅	❌	✅
Optimizer State	❌	❌	15 MB
Context Expansion	❌	❌	2×
File Size (9B)	18 GB	18 GB	8 GB
Universal	❌	❌	✅

One file. Training and inference. 86% smaller than keeping all three.

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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