Model overview
**A domain-specific LLM for cybersecurity** — built for ASOC, DFIR and detection-engineering work, and built to run on one machine, inside your own perimeter. Fine-tuned from `Qwen/Qwen3.6-35B-A3B`, a Mixture-of-Experts model with **35B total parameters but only ~3B active per token**. You get large-model domain depth at small-model inference cost: roughly **50 tokens/second on a single NVIDIA DGX Spark**, from a 20 GB file. Released free to the security community by **IMPERUM B.V.** **www.IMPERUM.io**, built in partnership with **Alican Kiraz**. https://huggingface.co/AlicanKiraz0
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。