Tabular Transformer trained on 177,110 Stonkfun launches (status='graduated' label, 2.7% positive, time-ordered split). Leak-free features only: create-time metadata (name/symbol length, fees, creator, hour/dow, website) + 5m early-window candle features, computed by stonkfunfeatures.py, the same code the live scorer uses. - 123,976 train / 26,567 val / 26,567 test (time-ordered)
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.
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.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also 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
model.safetensors: PyTorch SafeTensors 가중치model.onnx: WebAssembly 및 에지 런타임용 고속 ONNX 그래프model_guard_ultra_fp16.baar3: Zero-Copy Direct-mmap FP16 초고속 커널 바이너리config.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.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.onnx | Weights | 2.7 MB | 7155d93f508d |
| model.safetensors | Weights | 2.6 MB | 5b180bb8f583 |
| config.json | Configuration | 1.1 KB | — |
| DATASET_README.md | Documentation | 960 B | — |
| README.md | Documentation | 776 B | — |
| model_guard_ultra_fp16.baar3 | Other | 1.3 MB | c61bb27dfad0 |
| .gitattributes | Repository | 1.6 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 5.2 MB
Released by BAAR through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 5.2 MB |
| 16-bit | 0.0 GB |
| 8-bit | 0.0 GB |
| 4-bit | 0.0 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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