GoktugD/NanoSOC-Gemstone-4B-GGUF overview
NanoSOC Gemstone 4B — Edge SOC Analyst for 4 GB ARM Boards NanoSOC Gemstone 4B is a compact, text only security operations model tuned for structured event tri…
Runs locally from ~2.37 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | GoktugD/NanoSOC-Gemstone-4B-GGUF |
|---|---|
| Author | GoktugD |
| Pipeline | text-generation |
| License | other |
| Base model | Qwen/Qwen3.5-4B |
| Last modified | 2026-08-24T11:56:38.000Z |
Model README
---
license: other
language:
- en
- tr
library_name: llama.cpp
pipeline_tag: text-generation
base_model: Qwen/Qwen3.5-4B
tags:
- gguf
- qwen3.5
- cybersecurity
- soc
- incident-response
- edge-ai
- arm64
- t3-gemstone
- turkish
---
NanoSOC Gemstone 4B — Edge SOC Analyst for 4 GB ARM Boards
NanoSOC Gemstone 4B is a compact, text-only security operations model tuned for structured event triage on the T3 Gemstone O1 / AM67A class of edge hardware. It consumes normalized events from tools such as Zeek, Suricata or Wazuh and returns strict JSON with a decision, evidence, MITRE ATT&CK attribution and a human-reviewable next step.
This is the experimental / memory-tight variant. The recommended runtime context on a 4 GB board is 512 tokens.
> Hardware honesty: the GGUF builds were validated with llama.cpp on the build host. Physical T3 Gemstone latency and peak-RAM validation is still required. The board's 4 TOPS TIDL accelerators are not claimed to accelerate this LLM.
Frozen evaluation
The selected checkpoint was chosen only on a separate 400-record development set and then evaluated once on a frozen 1,000-record detection holdout with zero prompt overlap with training.
| Metric | Result |
|---|---:|
| Valid JSON | 100.00% |
| Decision accuracy | 87.50% |
| MITRE exact match | 87.50% |
| Detection recall | 99.00% |
| Precision | 61.68% |
| F1 | 76.01% |
| Specificity | 84.62% |
| False-positive rate | 15.38% |
Full aggregate evidence is in reports/frozen-score.json; frozen prompts and raw log contents are not redistributed.
Files
NanoSOC-Gemstone-4B-Q4_0.gguf— 2.37 GiB —9395fc16160241dd2c57243074ebffc57ed3668e51cc10d9818786d03bf27aa7NanoSOC-Gemstone-4B-Q4_K_M.gguf— 2.52 GiB —6e5e06d30a70ca8b8a5f70cf8e9ebacd569589440e4dff7443aa54db74563f6c
Q4_0 is the primary Gemstone package because it has predictable ARM NEON support and a tighter memory footprint. Q4_K_M is supplied for host-side quality comparison and boards with sufficient free RAM.
Host benchmark
Peak RSS observed in the short Q4_0 host benchmark: 4502.9 MiB. This is not an AM67A performance claim.
qwen35 4B Q4_0: 153.92 tokens/sqwen35 4B Q4_0: 9.29 tokens/s
Output contract
{
"guven": "...",
"kanit": ["..."],
"karar": "supheli",
"mitre_attack": ["T0000"],
"olay_tipi": "...",
"onerilen_adim": "..."
}
karar is constrained to supheli or yetersiz_kanit. The included JSON Schema can enforce this at generation time.
Run with llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cmake -S llama.cpp -B llama.cpp/build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON
cmake --build llama.cpp/build --target llama-cli -j 4
./deploy/run-gemstone.sh \
NanoSOC-Gemstone-4B-Q4_0.gguf \
./llama.cpp/build/bin/llama-cli
For T3 Gemstone itself, run deploy/build-on-gemstone.sh on the board first.
Intended use
- Offline triage of already-normalized SOC events
- Alert prioritization and evidence summarization
- MITRE ATT&CK suggestion with mandatory analyst review
- Edge deployments where events should remain local
Not intended for
- Raw packet/PCAP inspection
- Autonomous blocking, deletion, quarantine or remediation
- Replacing an IDS/IPS, SIEM correlation engine or human analyst
- Treating model confidence as calibrated probability
Training and provenance
- Base model: Qwen/Qwen3.5-4B
- Method: 4-bit NF4 QLoRA, rank 16
- Training mix: 9,129 structured SOC examples; 5,872 insufficient-evidence and 3,257 suspicious decisions
- Frozen holdout excluded from training and checkpoint selection
- Vision encoder removed; these are text-only GGUF packages
License and source notice
The Qwen3.5 base is Apache-2.0. The NanoSOC training mixture contains multiple source/provenance categories, including sources governed by their own terms. Therefore this repository is conservatively tagged license: other; review NOTICE.md and upstream source terms before commercial redistribution.
Turkish / Türkçe
Bu model ham PCAP okumaz; Zeek, Suricata veya Wazuh gibi sensörlerden gelen yapılandırılmış JSON olaylarını analiz eder. Üretilen aksiyonlar otomatik uygulanmamalı, analist onayından geçmelidir. 4 GB Gemstone kartında 4B sürümü için önerilen bağlam 512 tokendır.
Citation
@software{nanosoc_gemstone_4b,
author = {GoktugD},
title = {NanoSOC Gemstone 4B},
year = {2026},
url = {https://huggingface.co/GoktugD/NanoSOC-Gemstone-4B-GGUF}
}Run GoktugD/NanoSOC-Gemstone-4B-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models