---
language:
- en
- fr
license: apache-2.0
tags:
- gguf
- quantized
- cybersecurity
- edge-llm
- lora
- mistral
- elisar
model_name: ELISARCyberAIEdge7B-LoRA-GGUF
pipeline_tag: zero-shot-classification
datasets:
- custom
widget:
- text: What are the main threats targeting OT environments?
metrics:
- accuracy
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
---

# ELISARCyberAIEdge7B-LoRA-GGUF

[![GGUF](https://img.shields.io/badge/format-GGUF-blue.svg)](https://gguf.io/)
**Offline-ready, quantized LLaMA edge model for cybersecurity use cases**

---

## 📄 Paper Title
**ELISAR: An Adaptive Framework for Cybersecurity Risk Assessment Powered by GenAI**

## 👤 Authors
- Sabri ALLANI, PhD – AI & Cybersecurity Expert  
- Karam BOU-CHAAYA, PhD – AI & Cybersecurity Expert  
- Helmi RAIS – Global Practice Lead, Expleo France

## 📅 Date
May 31, 2025

## 🔗 Model Repository
[https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF](https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF)

## 📚 Publication
This work will be published by **Springer** in the following book:  
👉 [https://link.springer.com/chapter/10.1007/978-3-031-93598-5_16](https://link.springer.com/chapter/10.1007/978-3-031-93598-5_16)  
🗓️ **Expected publication date**: July 10, 2025

## 🧠 Summary
**ELISAR** is a fine-tuned LoRA model based on Mistral-7B, designed for contextualized cybersecurity risk assessment using Retrieval-Augmented Generation and Agentic AI capabilities. The model targets real-world use cases including:
- Threat modeling (Blue ELISAR)
- Offensive use-case generation (Red ELISAR)
- GRC compliance automation (GRC ELISAR)

## 📌 Use Cases
- ISO/IEC 42001 & NIS2 risk evaluation
- Threat scenario generation
- AI audit preparation and reporting
- Secure AI system design
- ....
---

## 📖 Overview

ELISARCyberAIEdge7B-LoRA-GGUF is a **LoRA-finetuned**, **GGUF-quantized** version of the Mistral-7B backbone tailored for **edge deployment in cybersecurity and blue-team AI scenarios**. Developed by Dr. Sabri Sallani (PhD), this model integrates:

📥 **Download model file**:  
[➡️ Click here to download `elisar_merged.gguf`](https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF/resolve/main/elisar_merged.gguf)  
(~5.13 GB GGUF quantized model for offline inference)

<p align="center"> <img src="https://huggingface.co/sallani/ELISARCyberAIEdge7B/resolve/main/elisar_robot_banner.png" alt="ELISAR - AI for Cybersecurity" width="700"/> </p>

1. **Base model**: Mistral-7B-v0.3 (FP16 / BF16)
2. **LoRA adapter**: `sallani/ELISARCyberAIEdge7B`
3. **Quantization**: Converted to GGUF format and optionally quantized to Q4\_K\_M (4-bit) for efficient inference on resource-constrained devices (NVIDIA T4, desktop GPUs, etc.).

This pipeline produces a single file (`elisar_merged.gguf`) of \~160 MiB that you can deploy **offline** using frameworks like [`llama.cpp`](https://github.com/ggml-org/llama.cpp) or run through minimal Torch-based inference.

**Key features:**

* **Compact (< 5 Go)** quantized GGUF file
* **Edge-friendly**: runs on CPU or low-end GPUs with fast cold-start
* **Cybersecurity-tuned**: trained to answer cybersecurity questions, perform log analysis, malware triage, and blue-team playbooks
* **Offline inference**: execute entirely without internet access

---

## 🚀 Quickstart

### 1. Download model files

```bash
# Clone or download the GGUF file directly:
wget https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF/resolve/main/elisar_merged.gguf -O elisar_merged.gguf
```

Alternatively, using the Hugging Face Hub CLI:

```bash
pip install huggingface_hub
huggingface-cli login  # enter HF_TOKEN
huggingface-cli repo clone sallani/ELISARCyberAIEdge7B-LoRA-GGUF
cd ELISARCyberAIEdge7B-LoRA-GGUF
tree
# ├── elisar_merged.gguf
# └── README.md
```

---

## 💿 Installation

#### 1. llama.cpp (Offline inference)

```bash
# Clone llama.cpp repository (if not already):
git clone --depth 1 https://github.com/ggml-org/llama.cpp.git
cd llama.cpp

# Build with GPU support (optional)
make clean
make CMAKE_CUDA=ON CMAKE_CUDA_ARCH=sm75

# Or build CPU-only:
# make
```

#### 2. Python (Transformers) – Optional hybrid inference

```bash
python3 -m venv venv
source venv/bin/activate
pip install torch transformers peft
```

---

## ⚡️ Usage Examples

### A. Offline inference with `llama.cpp`

```bash
cd llama.cpp
./main -m ../ELISARCyberAIEdge7B-LoRA-GGUF/elisar_merged.gguf -c 2048 -b 8 -t 8
```

### B. Python / Transformers + PEFT Inference (Hybrid)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch

model_id = "sallani/ELISARCyberAIEdge7B-LoRA-GGUF"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "You are a blue-team AI assistant. Analyze the following network log for suspicious patterns: ..."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

gen_config = GenerationConfig(
    temperature=0.7,
    top_p=0.9,
    max_new_tokens=256,
)
output_ids = model.generate(**inputs, **gen_config.to_dict())
answer = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(answer)
```

---

## 📦 File Structure

```
ELISARCyberAIEdge7B-LoRA-GGUF/
├── elisar_merged.gguf
└── README.md
```

---

## 🔧 Model Details & Training

* **Base**: Mistral-7B-v0.3 (7B params)
* **LoRA adapter**: `sallani/ELISARCyberAIEdge7B`
* **Quantization**: GGUF Q4\_K\_M, final size \~160 MiB
* **Training data**: CVEs, SAST, security logs, blue-team playbooks
* **License**: Apache 2.0

> *Developed by Dr. Sabri Sallani, PhD – Expert in Artificial Intelligence & Cybersecurity.*

---

## 📜 Prompt Guidelines

* Use instruction format: `### Instruction:` / `### Response:`
* Add relevant logs/code in prompt
* Not a replacement for certified analysts

---

## 📜 Citation

If you use this model or refer to the ELISAR framework in your research, please cite:

```
@incollection{elisar2025,
  author    = {Sabri Sallani and Karam Bou-Chaaya and Helmi Rais},
  title     = {ELISAR: An Adaptive Framework for Cybersecurity Risk Assessment Powered by GenAI},
  booktitle = {Communications in Computer and Information Science (CCIS, volume 2518)},
  publisher = {Springer},
  year      = {2025},
  note      = {To be published on July 10, 2025},
  url       = {https://link.springer.com/book/9783031935978}
}
```

Or simply cite:

> Sallani, S., Bou-Chaaya, K., & Rais, H. (2025). *ELISAR: An Adaptive Framework for Cybersecurity Risk Assessment Powered by GenAI*. In Springer Book on AI for Cybersecurity. Publication date: July 10, 2025. [https://link.springer.com/book/9783031935978](https://link.springer.com/book/9783031935978)

---

## 💬 Support & Contact

* 🗨️ [HF Discussion](https://huggingface.co/sallani/ELISARCyberAIEdge7B-LoRA-GGUF/discussions)
* 📧 [dr.sallani@CyberAI.com](mailto:dr.sallani@CyberAI.com)
* 🔗 [LinkedIn](https://www.linkedin.com/in/sabri-allani/)

---

*Thank you for using ELISARCyberAIEdge7B-LoRA-GGUF – helping secure your edge AI.*