Instructions to use mahmoudalyosify/Horus-OSINT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mahmoudalyosify/Horus-OSINT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mahmoudalyosify/Horus-OSINT",
	filename="llama-3-8b-instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mahmoudalyosify/Horus-OSINT with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mahmoudalyosify/Horus-OSINT:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mahmoudalyosify/Horus-OSINT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mahmoudalyosify/Horus-OSINT:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf mahmoudalyosify/Horus-OSINT:Q4_K_M

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 mahmoudalyosify/Horus-OSINT:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mahmoudalyosify/Horus-OSINT:Q4_K_M

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 mahmoudalyosify/Horus-OSINT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mahmoudalyosify/Horus-OSINT:Q4_K_M

Use Docker

docker model run hf.co/mahmoudalyosify/Horus-OSINT:Q4_K_M

LM Studio
Jan

vLLM

How to use mahmoudalyosify/Horus-OSINT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mahmoudalyosify/Horus-OSINT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mahmoudalyosify/Horus-OSINT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mahmoudalyosify/Horus-OSINT:Q4_K_M

Ollama
How to use mahmoudalyosify/Horus-OSINT with Ollama:
```
ollama run hf.co/mahmoudalyosify/Horus-OSINT:Q4_K_M
```

Unsloth Studio new

How to use mahmoudalyosify/Horus-OSINT 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 mahmoudalyosify/Horus-OSINT 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 mahmoudalyosify/Horus-OSINT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mahmoudalyosify/Horus-OSINT to start chatting

Docker Model Runner
How to use mahmoudalyosify/Horus-OSINT with Docker Model Runner:
```
docker model run hf.co/mahmoudalyosify/Horus-OSINT:Q4_K_M
```

Lemonade

How to use mahmoudalyosify/Horus-OSINT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mahmoudalyosify/Horus-OSINT:Q4_K_M

Run and chat with the model

lemonade run user.Horus-OSINT-Q4_K_M

List all available models

lemonade list

Horus-OSINT / README.md

mahmoudalyosify

Update README.md

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metadata

license: apache-2.0
library_name: unsloth
base_model: unsloth/Meta-Llama-3-8B-Instruct-bnb-4bit
tags:
  - llm
  - llama-3
  - osint
  - threat-intelligence
  - quantization
  - gguf
  - ollama
  - geopolitics
  - academic-project
pipeline_tag: text-generation
datasets:
  - GTD
  - GDELT
model_creator:
  - Mahmoud Alyosify
  - Sondos Omar
  - Mirna Embaby
language:
  - en

🦅 Horus-OSINT

A Cloud-Based Conversational Chatbot for Open-Source Intelligence (OSINT) and Global Threat Analysis using Fine-Tuned Lightweight LLMs.

📚 Model Description

Authors:
- Mahmoud Alyosify (Group 23 - CISC 886, Queen's University)
- Sondos Omar
- Mirna Embaby
Project Context:
Academic deliverable for CISC 886 – Cloud Computing, Queen's University.
Base Model:
unsloth/Meta-Llama-3-8B-Instruct-bnb-4bit
Architecture:
Llama-3 (8 Billion Parameters)
Quantization/Format:
4-bit Quantized (q4_k_m), GGUF format
Fine-tuning Technique:
PEFT (QLoRA) using Unsloth and HuggingFace TRL SFTTrainer
Language:
English
Main Applications:
Open-Source Intelligence (OSINT), Geopolitical Threat Analysis, Structured Military Reporting

🚀 Intended Uses

Threat Intelligence Analysis:
Query historical geopolitical events and regional instability from indexed records.
Automated Reporting:
Generates structured intelligence reports with clear sections: GEOPOLITICAL CONTEXT and THREAT ASSESSMENT.
Situational Awareness:
Analyze patterns of activities, severity levels, and trends according to historical data.

⚠️ Limitations & Ethical Considerations

No Real-Time Awareness:
All model outputs are restricted to knowledge present in training datasets (GTD & GDELT up to their latest records).
Hallucinations:
Outputs may contain errors or outdated information inherent to LLMs.
Not Suited for Critical Security Decisions:
This model is strictly for research and academic purposes – any use in security, military, or life-critical contexts requires thorough expert validation and oversight.

🏗️ Training & Data Pipeline Details

Big Data Engineering:
Sourced over 20M+ records from the Global Terrorism Database (GTD) and Global Database of Events, Language, and Tone (GDELT).
ETL Pipeline:
Deployed Apache Spark jobs via AWS EMR for cleaning and filtering large-scale event data.
Data Distillation:
Refined into an instruction-following dataset with 159,826 quality samples.
Compute Environment:
Fine-tuned on Google Colab T4 GPU (15–25 minutes runtime expected).
Cloud Storage:
Model artifacts are versioned on Amazon S3:
s3://horus-25bbdf-g23-bucket/models/horus-llama3-osint-Q4_K_M.gguf

💻 Usage Example

Horus-OSINT is exported as a compact GGUF model, ready out-of-the-box for Ollama:

Running Locally with Ollama

Download the llama-3-8b-instruct.Q4_K_M.gguf file.
Place a file named Modelfile in the same directory:
```
FROM ./llama-3-8b-instruct.Q4_K_M.gguf
```

In your terminal, run:

ollama create horus-osint -f Modelfile
ollama run horus-osint

🏷️ Citation

If you use this model in your research or applications, please cite:

@misc{horus_osint_2026,
  author = {Mahmoud Alyosify and Sondos Omar and Mirna Embaby},
  title = {Horus-OSINT: Cloud-Based OSINT and Threat Analysis using LLMs},
  year = {2026},
  publisher = {Hugging Face},
  institution = {Queen's University},
  url = {https://huggingface.co/mahmoudalyosify/Horus-OSINT}
}

📜 License

Apache 2.0 (subject to Meta’s Llama 3 Acceptable Use Policy)

📬 Contact

For questions or feedback, connect with the development team:

All technical details and contributor links are included for thorough documentation and professional presentation. Simply copy and use as your model card on Hugging Face!