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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	---
	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](https://www.linkedin.com/in/mahmoudalyosify/) (Group 23 - CISC 886, Queen's University)
	- [Sondos Omar](https://www.linkedin.com/in/sondos-omar-305s/)
	- [Mirna Embaby](https://www.linkedin.com/in/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](https://ollama.com/):

	### Running Locally with Ollama

	1. Download the `llama-3-8b-instruct.Q4_K_M.gguf` file.
	2. Place a file named `Modelfile` in the same directory:
	```dockerfile
	FROM ./llama-3-8b-instruct.Q4_K_M.gguf
	```
	3. In your terminal, run:
	```bash
	ollama create horus-osint -f Modelfile
	ollama run horus-osint
	```

	---

	## 🏷️ Citation

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

	```bibtex
	@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:
	- [Mahmoud Alyosify](https://www.linkedin.com/in/mahmoudalyosify/)
	- [Sondos Omar](https://www.linkedin.com/in/sondos-omar-305s/)
	- [Mirna Embaby](https://www.linkedin.com/in/mirna-embaby/)

	---

	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!