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Open-weight model · Tabular classification

BAAR4-guard

by BAAR aixk/BAAR4-guard

BAAR4-guard is an open-weight model for tabular classification from BAAR, released under Apache License 2.0. It has 644,381 parameters. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

이 저장소는 2017~2026 전주기 트래픽(CIC-IDS2017, CICDDoS2019, CICIoT2023, 2024~2026 AI Vectors)으로 훈련된 고속 인라인 AI 보안 방화벽 엔진입니다. 1. model.safetensors: PyTorch SafeTensors 가중치 2. model.onnx: WebAssembly 및 에지 런타임용 고속 ONNX 그래프 3.

Parameters644,381
Context—
Weights5.2 MB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve BAAR4-guard (644,381 parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 23, 2026.

BAAR4-guard on every accelerator the SAVRN Index prices, at every precision

Model Card

By BAAR, published under apache-2.0, revision 1febb6f3c9fa.

이 저장소는 2017~2026 전주기 트래픽(CIC-IDS2017, CICDDoS2019, CICIoT2023, 2024~2026 AI Vectors)으로 훈련된 고속 인라인 AI 보안 방화벽 엔진입니다. 1. model.safetensors: PyTorch SafeTensors 가중치 2. model.onnx: WebAssembly 및 에지 런타임용 고속 ONNX 그래프 3. modelguardultrafp16.baar3: Zero-Copy Direct-mmap FP16 초고속 커널 바이너리 4. config.json: Robust 스케일러 및 제로 트러스트 임계치 메타데이터

Read BAAR's full model card

BAAR4-guard Ultra (In-Line Multi-Era AI Threat Engine)

이 저장소는 2017~2026 전주기 트래픽(CIC-IDS2017, CICDDoS2019, CICIoT2023, 2024~2026 AI Vectors)으로 훈련된 고속 인라인 AI 보안 방화벽 엔진입니다.

포함된 배포 아티팩트

  1. model.safetensors : PyTorch SafeTensors 가중치
  2. model.onnx : WebAssembly 및 에지 런타임용 고속 ONNX 그래프
  3. model_guard_ultra_fp16.baar3 : Zero-Copy Direct-mmap FP16 초고속 커널 바이너리
  4. config.json : Robust 스케일러 및 제로 트러스트 임계치 메타데이터

Identity and Version

Repository
aixk/BAAR4-guard
Publisher
BAAR
Task
Tabular classification
Modality
Tabular
Library
Not stated by the source
Parameters
644,381 parameters
Languages
en, ko
Revision
1febb6f3c9fa8b34409e24f3ae6895090d200640
First published
2026-10-09
Last updated
2026-10-09

Files and Weights

7 files, 6.5 MB in total. The weights are 2 files totalling 5.2 MB in onnx, safetensors.

Weights2 files · 5.2 MB
Configuration1 file · 1.1 KB
Documentation2 files · 1.7 KB
Other1 file · 1.3 MB
Repository1 file · 1.6 KB
Every file
FileTypeSizeSHA-256
model.onnxWeights2.7 MB 7155d93f508d
model.safetensorsWeights2.6 MB 5b180bb8f583
config.jsonConfiguration1.1 KB —
DATASET_README.mdDocumentation960 B —
README.mdDocumentation776 B —
model_guard_ultra_fp16.baar3Other1.3 MB c61bb27dfad0
.gitattributesRepository1.6 KB —

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
5.2 MB
Download from BAAR

Released by BAAR through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published5.2 MB
16-bit0.0 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About BAAR4-guard

How much GPU memory does BAAR4-guard need?

About 0 GB at 16-bit and 0 GB at 4-bit: the weights (644,381 parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run BAAR4-guard on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use BAAR4-guard commercially?

Yes. BAAR4-guard is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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