--- 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)

ELISAR - AI for Cybersecurity

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.*