README.md

---
tags:
- autotrain
- text-generation-inference
- text-generation
- cybersécurity
- BLUE
- EDGEAI
- GRC
library_name: transformers
base_model: mistralai/Mistral-7B-Instruct-v0.3
widget:
- messages:
  - role: user
    content: What is your favorite condiment?
license: apache-2.0
language:
- en
- fr
---
# ELISARCyberAIEdge7B

> **Maintainer:** Dr. Sabri Sallani
> **Expertise:** AI Research & Cybersecurity
> **Adapter type:** LoRA (Low-Rank Adaptation)
> **Base model:** mistralai/Mistral-7B-v0.1 (FP16)
> **Intended use:** Offline edge deployment for CyberAI & Blue-Team scenarios
> **License:** Apache 2.0 (see LICENSE)

---

## 📖 Overview

**ELISARCyberAIEdge7B** is a LoRA adapter crafted by Dr. Sabri Sallani—AI & cybersecurity researcher—to specialize Mistral-7B for offline, on-device CyberAI and “Blue AI” (defensive) applications. Once merged with the FP16 base, you obtain a single \~5 GB GGUF that runs natively on edge hardware (e.g., Raspberry Pi 4, Jetson Nano, NVIDIA T4) without internet access.
<p align="center"> <img src="https://huggingface.co/sallani/ELISARCyberAIEdge7B/resolve/main/elisar_robot_banner.png" alt="ELISAR - AI for Cybersecurity" width="700"/> </p>
Key points:

* 🔧 **LoRA-only:** Contains low-rank delta-weights for Mistral-7B.
* 🛠️ **Edge-optimized:** Full merged GGUF runs entirely offline on typical edge GPUs/accelerators.
* 🚀 **Cybersecurity focus:** Fine-tuned on “ELISAR CyberAI Edge” corpus—vulnerability descriptions, incident reports, secure-coding examples, threat intelligence summaries.
* 👤 **Authored by Dr. Sabri Sallani:** Published under the ELISAR initiative.

---

## ⚙️ Installation

1. **Python dependencies**

   ```bash
   pip install transformers peft accelerate sentencepiece torch
   ```

2. *(Optional)* **llama.cpp + GGUF tools** (to merge and run offline)

   ```bash
   # Clone and install gguf-py
   git clone --depth 1 https://github.com/ggml-org/llama.cpp.git
   pip install ./llama.cpp/gguf-py
   pip install llama-cpp-python
   ```

   → Use these tools to merge LoRA + base weights into a single GGUF.

---

## 🐍 Usage

### 1. Inference with `transformers` + `PEFT` (online GPU/CPU)

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_ID    = "mistralai/Mistral-7B-v0.1"
ADAPTER_ID = "sallani/ELISARCyberAIEdge7B"

# 1) Load Mistral-7B base (FP16 or BF16) with automatic device placement
tokenizer = AutoTokenizer.from_pretrained(BASE_ID, use_fast=True)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_ID,
    torch_dtype="auto",
    device_map="auto"
)

# 2) Load LoRA adapter on top
model = PeftModel.from_pretrained(
    base_model,
    ADAPTER_ID,
    torch_dtype="auto",
    device_map="auto"
)
model.eval()

# 3) Perform inference
prompt = (
    "### Instruction:\n"
    "Propose a set of secure-coding guidelines for Python web applications.\n"
    "### Response:\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.8,
        top_p=0.9,
        repetition_penalty=1.1
    )

print(tokenizer.decode(out[0], skip_special_tokens=True))
```

* **device\_map="auto"** places weights on GPU/CPU automatically (FP16 when supported).
* Adjust sampling parameters (`temperature`, `top_p`, `repetition_penalty`) for your use case.

### 2. Offline Edge Deployment via `llama.cpp` (Merged GGUF)

1. **Merge LoRA + base into a single GGUF**

   ```bash
   python3 llama.cpp/convert_lora_to_gguf.py \
     sallani/ELISARCyberAIEdge7B \                # LoRA repo or local folder
     --base-model-id mistralai/Mistral-7B-v0.1 \  # HF ID of FP16 base
     --outfile elisar_full_f16.gguf               # Output GGUF (~5 GB)
   ```

   * The script pulls the FP16 base automatically from HF, applies LoRA deltas, and writes a merged GGUF.

2. **Run inference on edge**

   * Copy `elisar_full_f16.gguf` to your edge device (Jetson Nano, Raspberry Pi 4 + GPU, NVIDIA T4).
   * Use `llama.cpp` binary to run:

     ```bash
     ./llama.cpp/main \
       -m elisar_full_f16.gguf \
       -p "### Instruction: Audit the following log entries for suspicious activity.\n---\n<log lines>\n---\n### Response:" \
       --temp 0.7 \
       --repeat_penalty 1.1 \
       --n 128
     ```
   * **No internet** is required once the GGUF is on-device.

---

## 📐 Model Details

* **Base architecture:**
  Mistral-7B-v0.1 (40 transformer layers, 4096-dim embedding, 32 heads, causal LM).

* **LoRA configuration:**

  * Rank = 64, α = 16
  * Applied to Q/K/V and feed-forward projections
  * Adapter snapshots ≈ 168 MB

* **Training corpus (ELISAR CyberAI Edge):**

  * Public vulnerability databases (CVE entries, CVSS scoring).
  * Real-world incident reports (MITRE ATT\&CK red vs. blue logs).
  * Secure-coding patterns (OWASP Top 10, SAST examples).
  * Blue-team playbooks and defensive strategies.

* **Hyperparameters:**

  * Learning rate = 1e-4, batch size = 16 per GPU, 3 epochs on 8×A100 (FP16).
  * Validation on unseen CVE descriptions and red-team prompts.

* **Merged GGUF (FP16):**

  * \~5 GB total after merging and trimming unnecessary metadata for on-device use.

---

## 🔖 Prompt Guidelines

* **Structured prompt**

  ```
  ### Instruction:
  <clear cybersecurity or defensive AI task>
  ### Response:
  ```
* **Recommended sampling**

  * `temperature=0.7–0.9` for balanced creativity.
  * `top_p=0.9` for nucleus sampling.
  * `repetition_penalty=1.1` to reduce loops.

---


---
language: en
license: apache-2.0
tags:
  - gguf
  - quantized
  - cybersecurity
  - edge-llm
  - lora
  - mistral
  - elisar
model_name: ELISARCyberAIEdge7B-LoRA-GGUF
pipeline_tag: text-generation
datasets:
  - custom
widget:
  - text: "What are the main threats targeting OT environments?"


## 📊 Stats & Adoption

* 🔄 Download tracking: [Enabled](https://huggingface.co/sallani/ELISARCyberAIEdge7B)
* 📥 Total downloads (last 30 days): _auto-updated by HF_
* 🧪 Being tested on:
  - Jetson Nano (Ubuntu 20.04, CUDA 11.4)
  - Raspberry Pi 4 (with Coral TPU)
  - NVIDIA T4 + LLaMA.cpp

Want to share your benchmarks? Open an [Issue](https://huggingface.co/sallani/ELISARCyberAIEdge7B/issues) or pull request.


## ⚠️ License & Citation

* **License:** Apache 2.0 (see [LICENSE](LICENSE)).
* **Attribution:**

  > Sallani, S. (2025). *ELISARCyberAIEdge7B: LoRA adapter for Mistral-7B specializing in offline CyberAI Edge tasks*. Hugging Face Model Hub: `sallani/ELISARCyberAIEdge7B`.

---

## 🛠️ Support & Contact

* **Report issues or feature requests:**
  [https://huggingface.co/sallani/ELISARCyberAIEdge7B/issues](https://huggingface.co/sallani/ELISARCyberAIEdge7B/issues)

* **Contact the author:**
  Dr. Sabri Sallani
  • GitHub: [@sallani](https://github.com/sallani)
  • Email: `sabri.sallani@cyberaiedge.com`
  • LinkedIn: [linkedin.com/in/sabri-sallani](https://linkedin.com/in/sabri-sallani)

Thank you for using **ELISARCyberAIEdge7B**. This adapter empowers secure, offline AI at the edge for next-gen CyberAI and Blue-Team applications.