README.md

---
base_model: Qwen/Qwen3-4B
language:
  - en
  - ar
  - tr
license: apache-2.0
library_name: gguf
tags:
  - gguf
  - qwen3
  - conversational
  - osint
  - cybersecurity
  - fine-tuned
  - security
  - intelligence
pipeline_tag: text-generation
model_name: Qwen-OSINT
quantized_by: aab20abdullah
---
# Qwen-OSINT

<div align="center">
  <img src="https://img.shields.io/badge/Model-Qwen2.5--7B-blue?style=flat-square" alt="Model">
  <img src="https://img.shields.io/badge/License-Apache%202.0-green?style=flat-square" alt="License">
  <img src="https://img.shields.io/badge/Task-OSINT-orange?style=flat-square" alt="Task">
  <img src="https://img.shields.io/badge/Dataset-Multi--source-red?style=flat-square" alt="Dataset">
</div>

---

## 📋 Table of Contents

- [Overview](#overview)
- [Features](#features)
- [Model Details](#model-details)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Usage Examples](#usage-examples)
- [Ethical Guidelines](#ethical-guidelines)
- [Limitations](#limitations)
- [License](#license)
- [Acknowledgments](#acknowledgments)

---

## 🎯 Overview

**Qwen-OSINT** is a specialized large language model fine-tuned from [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) specifically designed for Open Source Intelligence (OSINT) operations. This model leverages advanced natural language processing capabilities to assist security researchers, analysts, and investigators in gathering, analyzing, and synthesizing information from publicly available sources.

### What is OSINT?

Open Source Intelligence (OSINT) refers to the practice of collecting and analyzing information from publicly available sources to support decision-making processes. This includes data from:

- 🌐 Social media platforms
- 📰 News articles and publications
- 🔍 Search engines and databases
- 💼 Professional networks
- 🌐 Public records and government databases

---

## ✨ Features

| Feature | Description |
|---------|-------------|
| 🔎 **Advanced Search Analysis** | Efficiently analyzes search queries and identifies relevant intelligence sources |
| 📊 **Data Synthesis** | Consolidates information from multiple sources into coherent summaries |
| 🔐 **Security Analysis** | Supports threat analysis and vulnerability assessment tasks |
| 📝 **Report Generation** | Generates structured intelligence reports in various formats |
| 🌐 **Multi-language Support** | Processes and analyzes content in multiple languages |
| 🛡️ **Ethical Compliance** | Built with safety guidelines to ensure responsible use |

---

## 📊 Model Details

| Attribute | Value |
|-----------|-------|
| **Base Model** | Qwen2.5-7B-Instruct |
| **Framework** | Transformers (Hugging Face) |
| **Training Method** | Supervised Fine-tuning (SFT) |
| **Vocabulary Size** | 151,669 tokens |
| **Architecture** | Transformer-based Decoder |
| **Precision** | FP16 / INT8 compatible |

### Training Configuration

```
- Learning Rate: 2e-5
- Batch Size: 8
- Epochs: 3
- Warmup Steps: 100
- Max Sequence Length: 8192
```

---

## 🔧 Installation

### Prerequisites

```
Python >= 3.8
PyTorch >= 2.0
transformers >= 4.35.0
accelerate >= 0.20.0
bitsandbytes >= 0.40.0 (for quantization)
```

### Install Dependencies

```bash
pip install transformers torch accelerate bitsandbytes
```

### Download the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "aab20abdullah/qwen_OSINT"

# Download tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Download model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    trust_remote_code=True
)
```

---

## 🚀 Quick Start

### Basic Usage

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

model_name = "aab20abdullah/qwen_OSINT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

def generate_intelligence(prompt, max_length=512):
    messages = [{"role": "user", "content": prompt}]
    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer([text], return_tensors="pt").to("cuda")
    
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_length,
        temperature=0.7,
        top_p=0.9
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("assistant")[-1].strip()

# Example
result = generate_intelligence("Analyze the key elements of a threat intelligence report.")
print(result)
```

### Quantized Version (Lower Memory Usage)

```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=quantization_config,
    device_map="auto",
    trust_remote_code=True
)
```

---

## 💡 Usage Examples

### Example 1: Search Query Analysis

```python
prompt = """Analyze the following search query and suggest improvements for OSINT research:
Query: "site:linkedin.com cybersecurity analyst" """

result = generate_intelligence(prompt)
print(result)
```

### Example 2: Data Source Evaluation

```python
prompt = """Evaluate the reliability and credibility of the following OSINT sources:
1. Government statistical databases
2. Academic research papers
3. Social media platforms
4. Open-source code repositories"""

result = generate_intelligence(prompt)
print(result)
```

### Example 3: Threat Analysis Framework

```python
prompt = """Using the MITRE ATT&CK framework, analyze potential threat vectors for:
- Phishing attacks
- Network intrusion
- Data exfiltration

Provide recommendations for detection and prevention.""" 

result = generate_intelligence(prompt)
print(result)
```

---

## 🛡️ Ethical Guidelines

> ⚠️ **IMPORTANT**: This model is designed for **legitimate OSINT research** only.

### Acceptable Use Cases ✅

- 🔍 Security research and vulnerability assessment
- 📊 Threat intelligence analysis
- 🛡️ Organizational security posture evaluation
- 📚 Academic research in cybersecurity
- 🏢 Corporate due diligence

### Prohibited Use Cases ❌

- 🚫 Unauthorized surveillance
- 🚫 Invasion of privacy
- 🚫 Harassment or stalking
- 🚫 Illegal activities
- 🚫 Content generation for malicious purposes

### Responsible Use Principles

1. **Transparency**: Clearly identify yourself when conducting OSINT operations
2. **Legality**: Ensure compliance with applicable laws and regulations
3. **Proportionality**: Collect only information necessary for your objectives
4. **Security**: Protect collected data appropriately
5. **Accountability**: Maintain records of your OSINT activities

---

## ⚠️ Limitations

| Limitation | Description |
|------------|-------------|
| ⚡ **Computational Resources** | Requires GPU with sufficient VRAM for optimal performance |
| 🎯 **Accuracy** | May generate plausible but incorrect information - always verify |
| 🌍 **Language Coverage** | Best performance in English; other languages may vary |
| 📅 **Knowledge Cutoff** | Training data has a knowledge cutoff date |
| 🔒 **Sensitive Data** | Not designed to handle highly classified or sensitive information |

---

## 📄 License

This model is released under the **Apache 2.0 License**.

```
Copyright 2024 aab20abdullah

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0
```

### Base Model License

The base model [Qwen2.5](https://huggingface.co/Qwen/Qwen2.5-7B) is licensed under the [Qwen Research License](https://github.com/QwenLM/Qwen2/blob/main/Qwen2.5_LICENSE).

---

## 🙏 Acknowledgments

- **Alibaba Cloud** - For developing the Qwen2.5 model architecture
- **Hugging Face** - For providing the model hosting infrastructure
- **Open Source Community** - For continuous contributions to AI safety and ethics

---

## 📬 Contact

- **Model Repository**: [huggingface.co/aab20abdullah/qwen_OSINT](https://huggingface.co/aab20abdullah/qwen_OSINT)
- **Author**: [aab20abdullah](https://huggingface.co/aab20abdullah)

---

## 📝 Citation

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

```bibtex
@model{qwen_osint,
  author = {aab20abdullah},
  title = {Qwen-OSINT: A Specialized Model for Open Source Intelligence},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/aab20abdullah/qwen_OSINT}
}
```

---

<div align="center">
  <p>⭐ If you find this model useful, please consider giving it a star!</p>
  <p>Made with ❤️ for the OSINT community</p>
</div>