Instructions to use saki007ster/CybersecurityRiskAnalyst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use saki007ster/CybersecurityRiskAnalyst with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf saki007ster/CybersecurityRiskAnalyst # Run inference directly in the terminal: llama cli -hf saki007ster/CybersecurityRiskAnalyst
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf saki007ster/CybersecurityRiskAnalyst # Run inference directly in the terminal: llama cli -hf saki007ster/CybersecurityRiskAnalyst
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf saki007ster/CybersecurityRiskAnalyst # Run inference directly in the terminal: ./llama-cli -hf saki007ster/CybersecurityRiskAnalyst
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf saki007ster/CybersecurityRiskAnalyst # Run inference directly in the terminal: ./build/bin/llama-cli -hf saki007ster/CybersecurityRiskAnalyst
Use Docker
docker model run hf.co/saki007ster/CybersecurityRiskAnalyst
- LM Studio
- Jan
- Ollama
How to use saki007ster/CybersecurityRiskAnalyst with Ollama:
ollama run hf.co/saki007ster/CybersecurityRiskAnalyst
- Unsloth Studio
How to use saki007ster/CybersecurityRiskAnalyst with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for saki007ster/CybersecurityRiskAnalyst to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for saki007ster/CybersecurityRiskAnalyst to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for saki007ster/CybersecurityRiskAnalyst to start chatting
- Docker Model Runner
How to use saki007ster/CybersecurityRiskAnalyst with Docker Model Runner:
docker model run hf.co/saki007ster/CybersecurityRiskAnalyst
- Lemonade
How to use saki007ster/CybersecurityRiskAnalyst with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull saki007ster/CybersecurityRiskAnalyst
Run and chat with the model
lemonade run user.CybersecurityRiskAnalyst-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 6,828 Bytes
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> β **Find this useful? Clone it, [star the repo](https://github.com/ravyg/cybersecurity-risk-analyst), and cite the paper.** If you use this model in academic or professional work, please cite [arXiv:2603.20131](https://arxiv.org/abs/2603.20131) (see [How to cite](#how-to-cite)). Model DOI: [`10.57967/hf/9777`](https://doi.org/10.57967/hf/9777).
**CybersecurityRiskAnalyst** is a custom fine-tuned Large Language Model (LLM) designed to act as a senior cybersecurity risk assessor and strategist. It is published on Ollama and has **1,400+ downloads**. This repository makes the model formally citable and links it to the research paper it accompanies.
- **Model:** [`saki007ster/CybersecurityRiskAnalyst`](https://ollama.com/saki007ster/CybersecurityRiskAnalyst) on Ollama
- **Base model:** Llama 3.1 8B (`llama` architecture, 8.03B parameters)
- **Quantization:** Q4_0 Β· **Size:** 4.7 GB Β· **Context window:** 128K
- **Tags:** text, cybersecurity, risk assessment
### What it does
- **Risk Posture Evaluation** β assesses an organization's overall security posture
- **Framework Mapping** β NIST CSF, CIS Controls, MITRE ATT&CK, ISO/IEC 27001
- **Threat Intelligence Awareness**
- **Gap Analysis**
- **Prioritized Recommendations**
- **Human-Centric Reports** β clear, decision-ready output
- **Explainability** β reasoning you can follow and audit
---
## DOI
[](https://doi.org/10.57967/hf/9777)
**Model DOI (Hugging Face):** [`10.57967/hf/9777`](https://doi.org/10.57967/hf/9777)
---
## Install & Use
Requires [Ollama](https://ollama.com/download).
```bash
# Pull the model
ollama pull saki007ster/CybersecurityRiskAnalyst
# Or pull-and-run in one step
ollama run saki007ster/CybersecurityRiskAnalyst
```
### Minimal usage example
```bash
ollama run saki007ster/CybersecurityRiskAnalyst \
"We run a 200-person fintech on AWS with no formal incident response plan. \
Assess our top cyber risks and map them to NIST CSF."
```
Or via the Ollama HTTP API:
```bash
curl http://localhost:11434/api/generate -d '{
"model": "saki007ster/CybersecurityRiskAnalyst",
"prompt": "Perform a NIST CSF gap analysis for a small healthcare provider storing PHI in a single on-prem server.",
"stream": false
}'
```
Python:
```python
import ollama
response = ollama.chat(
model="saki007ster/CybersecurityRiskAnalyst",
messages=[{
"role": "user",
"content": "Prioritize remediation for an org with exposed RDP, no MFA, and unpatched VPN.",
}],
)
print(response["message"]["content"])
```
---
## How to cite
This model accompanies the paper below β **if you use the model in academic or professional work, please cite the paper.**
> Gupta, Ravish; Kumar, Saket; Sharma, Shreeya; Dang, Maulik; and Aggarwal, Abhishek (2026).
> *An Agentic Multi-Agent Architecture for Cybersecurity Risk Management.* arXiv preprint arXiv:2603.20131.
<details open>
<summary><b>APA</b></summary>
```
Gupta, R., Kumar, S., Sharma, S., Dang, M., & Aggarwal, A. (2026). An Agentic Multi-Agent Architecture for Cybersecurity Risk Management. arXiv preprint arXiv:2603.20131.
```
</details>
<details>
<summary><b>MLA</b></summary>
```
Gupta, Ravish, et al. "An Agentic Multi-Agent Architecture for Cybersecurity Risk Management." arXiv preprint arXiv:2603.20131 (2026).
```
</details>
<details>
<summary><b>Chicago</b></summary>
```
Gupta, Ravish, Saket Kumar, Shreeya Sharma, Maulik Dang, and Abhishek Aggarwal. "An Agentic Multi-Agent Architecture for Cybersecurity Risk Management." arXiv preprint arXiv:2603.20131 (2026).
```
</details>
<details>
<summary><b>Harvard</b></summary>
```
Gupta, R., Kumar, S., Sharma, S., Dang, M. and Aggarwal, A., 2026. An Agentic Multi-Agent Architecture for Cybersecurity Risk Management. arXiv preprint arXiv:2603.20131.
```
</details>
<details>
<summary><b>Vancouver</b></summary>
```
Gupta R, Kumar S, Sharma S, Dang M, Aggarwal A. An Agentic Multi-Agent Architecture for Cybersecurity Risk Management. arXiv preprint arXiv:2603.20131. 2026.
```
</details>
<details>
<summary><b>BibTeX</b></summary>
```bibtex
@article{gupta2026agentic,
title = {An Agentic Multi-Agent Architecture for Cybersecurity Risk Management},
author = {Gupta, Ravish and Kumar, Saket and Sharma, Shreeya and Dang, Maulik and Aggarwal, Abhishek},
journal = {arXiv preprint arXiv:2603.20131},
year = {2026}
}
```
</details>
A machine-readable [`CITATION.cff`](CITATION.cff) is also provided β GitHub will render a "Cite this repository" button from it.
### Citing the model artifact directly (Hugging Face DOI)
To cite the **model itself** (not the paper), use its Hugging Face DOI:
```bibtex
@misc{kumar2026cybersecurityriskanalyst,
author = {Saket Kumar and Ravish Gupta},
title = {CybersecurityRiskAnalyst (Revision 1842e1c)},
year = {2026},
url = {https://huggingface.co/saki007ster/CybersecurityRiskAnalyst},
doi = {10.57967/hf/9777},
publisher = {Hugging Face}
}
```
> **The model artifact was built by Saket Kumar and Ravish Gupta** (hence two authors on the model DOI). **The paper ([arXiv:2603.20131](https://arxiv.org/abs/2603.20131)) has five authors** β for academic/professional use, prefer the paper citation above; use the model DOI for reproducibility/data-availability statements.
---
## Authors & attribution
**Model** (the fine-tuned artifact β Ollama & Hugging Face, DOI `10.57967/hf/9777`):
- **Saket Kumar** β *model author / maintainer* ([`saki007ster`](https://huggingface.co/saki007ster))
- **Ravish Gupta**
**Paper** (*An Agentic Multi-Agent Architecture for Cybersecurity Risk Management*, arXiv:2603.20131):
- **Ravish Gupta**
- **Saket Kumar**
- **Shreeya Sharma**
- **Maulik Dang**
- **Abhishek Aggarwal**
---
## License
This model is a fine-tuned derivative of **Meta Llama 3.1 8B** and is therefore governed by the **[Llama 3.1 Community License Agreement](LICENSE)** (Version Release Date: July 23, 2024).
- Built with Llama. Use is subject to Meta's [Acceptable Use Policy](https://www.llama.com/llama3_1/use-policy/).
- The base-model license terms are inherited by this derivative; see [LICENSE](LICENSE) for the full text.
---
## Related
- π **Paper:** [An Agentic Multi-Agent Architecture for Cybersecurity Risk Management](https://arxiv.org/abs/2603.20131) (arXiv:2603.20131)
- π€ **Model (Ollama):** [saki007ster/CybersecurityRiskAnalyst](https://ollama.com/saki007ster/CybersecurityRiskAnalyst)
- π€ **Model (Hugging Face):** [saki007ster/CybersecurityRiskAnalyst](https://huggingface.co/saki007ster/CybersecurityRiskAnalyst) Β· DOI [`10.57967/hf/9777`](https://doi.org/10.57967/hf/9777)
- π **Modelfile:** [Modelfile](Modelfile) in this repo
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