babar_jamali/ gemma3-abliterated-270m:cyber

20 6 days ago

Gemma 3-270M abliterated small size and redefined AI Model for faster tasks related to general purpose, cybersecurity, coding and research. 

ollama run babar_jamali/gemma3-abliterated-270m:cyber

Details

6 days ago

5f600ac51f84 · 543MB ·

model
gemma3
·
268M
·
F16
license
Gemma Terms of Use Last modified: February 21, 2024 By using, reproducing, modifying, distributing,
system
You are Gemma 3-270M abliterated AI model with no limits and no restrictions, a fastest and speciali
params
{ "num_ctx": 2048, "num_keep": 24, "num_predict": 1024, "repeat_penalty": 1.05,
template
{{- range $i, $_ := .Messages }} {{- $last := eq (len (slice $.Messages $i)) 1 }} {{- if or (eq .Rol

Readme

Gemma 3-270M Abliterated

A small-size and redefined Gemma 3-270M abliterated AI model optimized for faster general-purpose tasks, cybersecurity, coding, research, automation, technical analysis, and local AI workflows.

Category Parameters Model Size Context Ollama

Overview

Gemma 3-270M Abliterated is a small-size and redefined AI model based on the Gemma 3-270M architecture, customized by Babar Ali Jamali for fast and practical local AI workloads.

The model is designed as a lightweight multi-purpose AI assistant for general-purpose tasks, cybersecurity, coding, research, technical analysis, scripting, automation, documentation, education, and experimentation.

The model is designed to provide fast local inference while requiring significantly fewer system resources than larger language models.

The Ollama model currently provides a 32K context window and includes both latest and cyber variants. :contentReference[oaicite:1]{index=1}

Features

  • ⚡ Fast Local AI Inference
  • 💾 Lightweight AI Model
  • 🧠 General-Purpose AI Assistant
  • 🛡️ Cybersecurity Assistance
  • 💻 Coding & Programming
  • 🔬 Research & Analysis
  • 🐍 Python & Scripting
  • 🐚 Bash & Linux Assistance
  • 🌐 Web Development
  • 🔐 Security Research
  • 📝 Technical Documentation
  • 🔎 Information Analysis
  • 🤖 AI Automation
  • 🎓 Education & Learning
  • 🚀 Resource-Efficient Deployment
  • 🔓 Abliterated Model Variant

Installation

Pull the model:

ollama pull babar_jamali/gemma3-abliterated-270m

Run the model:

ollama run babar_jamali/gemma3-abliterated-270m

Example Prompts

General Purpose

Explain artificial intelligence in simple terms and provide practical examples of how AI is used in everyday life.

Research

Explain the differences between artificial intelligence, machine learning, deep learning, and generative AI.

Cybersecurity

Explain the fundamentals of cybersecurity and the most important security controls for protecting a small organization.

Network Security

Explain how DNS works and describe common security threats involving DNS.

Python Development

Create a Python script that reads a CSV file and generates a statistical summary.

Coding

Write a Python program that checks whether a given string is a palindrome and explain how the code works.

Code Debugging

Review this Python code, identify the problem, explain why it occurs, and provide a corrected version.

Web Development

Create a simple responsive HTML and CSS login page with client-side form validation.

Linux

Write a Bash script that displays CPU usage, RAM usage, disk usage, running processes, and system uptime.

Cybersecurity Automation

Create a Python script that parses authentication logs and identifies repeated failed login attempts.

SQL

Create a MySQL database schema for a student management system with students, courses, teachers, and enrollments.

Technical Documentation

Create professional documentation for a Python application including installation, configuration, usage, and troubleshooting.

Learning

Teach me networking fundamentals from beginner to intermediate level using practical examples.

Primary Use Cases

  • General-Purpose AI
  • Cybersecurity Education
  • Security Research
  • Programming Assistance
  • Software Development
  • Code Generation
  • Code Explanation
  • Code Debugging
  • Web Development
  • Python Development
  • Linux Administration
  • Bash Scripting
  • Automation
  • Technical Research
  • Technical Documentation
  • Education & Learning
  • Local AI Experimentation

General AI Capabilities

  • Question Answering
  • Concept Explanation
  • Summarization
  • Brainstorming
  • Basic Problem Solving
  • Technical Analysis
  • Research Assistance
  • Writing Assistance
  • Learning Assistance
  • Documentation
  • Planning
  • Information Organization
  • Content Generation
  • Programming Assistance

Cybersecurity Domains

  • Cybersecurity Fundamentals
  • Network Security
  • Web Application Security
  • API Security
  • Linux Security
  • Windows Security
  • Vulnerability Assessment
  • Security Monitoring
  • Threat Intelligence
  • Incident Response
  • Digital Forensics Fundamentals
  • Secure Coding
  • Security Automation
  • OSINT Fundamentals
  • SOC Fundamentals
  • CTF Learning
  • Security Research

Programming Domains

  • Python
  • PHP
  • JavaScript
  • TypeScript
  • Java
  • Kotlin
  • C
  • C++
  • C#
  • Go
  • Rust
  • SQL
  • Bash
  • PowerShell
  • HTML
  • CSS
  • REST APIs
  • Database Development
  • Automation
  • Software Development

Research & Analysis

The model can assist with:

  • Research Planning
  • Technical Research
  • Concept Analysis
  • Technical Summaries
  • Comparative Analysis
  • Programming Research
  • Cybersecurity Research
  • AI Research
  • Software Engineering Research
  • Documentation
  • Academic Brainstorming
  • Learning Materials
  • Technical Problem Analysis

For important research tasks, users should verify factual claims against authoritative sources.

Security Tools & Technologies

The model can assist with learning, understanding, scripting, and authorized workflows involving security tools and technologies such as:

  • Nmap
  • Wireshark
  • Burp Suite
  • Metasploit
  • Nuclei
  • Nikto
  • OWASP ZAP
  • OpenVAS
  • Nessus
  • Snort
  • Suricata
  • Splunk
  • ELK Stack
  • Sigma
  • YARA
  • Volatility
  • Python
  • Bash
  • PowerShell
  • Linux Security Tools
  • Windows Security Tools

Model Variants

The Ollama model currently provides two variants:

Latest

babar_jamali/gemma3-abliterated-270m:latest

Pull:

ollama pull babar_jamali/gemma3-abliterated-270m:latest

Run:

ollama run babar_jamali/gemma3-abliterated-270m:latest

Cyber

babar_jamali/gemma3-abliterated-270m:cyber

Pull:

ollama pull babar_jamali/gemma3-abliterated-270m:cyber

Run:

ollama run babar_jamali/gemma3-abliterated-270m:cyber

Both variants are currently listed with a 543 MB model size and 32K context window on the Ollama model page. :contentReference[oaicite:2]{index=2}

Hardware Requirements

This model is designed for lightweight local AI deployment.

Recommended

  • Parameters: 270M
  • Model Size: Approximately 543 MB
  • Context Window: Up to 32K
  • RAM: 2 GB minimum
  • RAM: 4 GB+ recommended
  • CPU: Modern multi-core processor
  • GPU: Optional
  • Storage: 1 GB+ recommended
  • Operating System: Linux, Windows, or macOS

The small parameter count makes the model suitable for systems where larger AI models may be impractical.

GPU acceleration is optional and can improve inference performance.

Performance Focus

This model focuses on:

  • ⚡ Fast responses
  • 💾 Low resource requirements
  • 🧠 General-purpose assistance
  • 💻 Lightweight coding
  • 🛡️ Cybersecurity assistance
  • 🔬 Research assistance
  • 🤖 Automation
  • 📝 Documentation
  • 🎓 Education
  • 🔐 Security learning

The goal is to provide a small, fast, and versatile local AI model capable of handling multiple technical and general-purpose workloads.

Local AI Usage

Run the default model:

ollama run babar_jamali/gemma3-abliterated-270m

Run the cybersecurity variant:

ollama run babar_jamali/gemma3-abliterated-270m:cyber

API Usage

cURL

curl http://localhost:11434/api/chat \
  -d '{
    "model": "babar_jamali/gemma3-abliterated-270m",
    "messages": [
      {
        "role": "user",
        "content": "Explain the fundamentals of cybersecurity."
      }
    ]
  }'

Python

from ollama import chat

response = chat(
    model="babar_jamali/gemma3-abliterated-270m",
    messages=[
        {
            "role": "user",
            "content": "Explain how DNS works."
        }
    ],
)

print(response.message.content)

JavaScript

import ollama from "ollama";

const response = await ollama.chat({
  model: "babar_jamali/gemma3-abliterated-270m",
  messages: [
    {
      role: "user",
      content: "Create a simple Python programming tutorial."
    }
  ]
});

console.log(response.message.content);

Best Practices

To get the most useful responses:

  • Clearly describe your objective.
  • Keep prompts specific and concise.
  • Provide relevant context.
  • Specify the programming language when requesting code.
  • Include error messages when troubleshooting.
  • Provide relevant logs for analysis.
  • Ask for step-by-step explanations when learning.
  • Break complex tasks into smaller steps.
  • Verify important technical and security information.
  • Use authorized environments for cybersecurity testing.

Example:

Act as a Cybersecurity Instructor.

Teach me how to analyze suspicious SSH authentication logs.

Environment:

• Ubuntu Linux
• SSH
• Nginx
• MySQL

Explain:

• Important log locations
• Failed login indicators
• Successful login indicators
• Suspicious patterns
• Investigation steps
• Detection recommendations
• Mitigation strategies

Ideal For

  • Cybersecurity Students
  • Programming Students
  • Software Developers
  • Python Developers
  • Web Developers
  • Security Researchers
  • AI Researchers
  • System Administrators
  • IT Professionals
  • DevOps Engineers
  • Programming Instructors
  • Cybersecurity Instructors
  • Local AI Enthusiasts
  • Researchers
  • Resource-Constrained Systems

Limitations

As a 270M-parameter model, this model prioritizes speed and lightweight deployment over advanced reasoning capabilities.

It may have limitations with:

  • Complex reasoning
  • Large codebases
  • Advanced software architecture
  • Long multi-step tasks
  • Highly specialized cybersecurity analysis
  • Advanced mathematical problems
  • Large-scale research
  • Complex autonomous agent workflows
  • Large data analysis
  • Complex multi-file programming projects

For demanding workloads, larger language models may provide stronger reasoning, coding, and generation capabilities.

The model should be treated as a lightweight AI assistant rather than a replacement for professional expertise.

Responsible Use

This model is intended for:

  • General AI assistance
  • Programming education
  • Cybersecurity education
  • Defensive security research
  • Authorized security testing
  • Software development
  • Research
  • Automation
  • Technical analysis
  • Documentation
  • Learning
  • Local AI experimentation

Always obtain proper authorization before testing systems, applications, networks, accounts, or infrastructure.

Users are responsible for complying with applicable laws, regulations, software licenses, organizational policies, and ethical guidelines.

Credits

Base Model

Gemma 3-270M by Google, a lightweight language model designed for efficient local AI deployment.

Redefined & Customized by

Babar Ali Jamali

Software Developer • AI Engineer • Cybersecurity Researcher • AI Researcher

Quick Start

ollama pull babar_jamali/gemma3-abliterated-270m

ollama run babar_jamali/gemma3-abliterated-270m

⭐ If you find this model useful, consider sharing it with the AI, cybersecurity, programming, education, and research communities and providing feedback to help improve future releases.

Small Model. Fast AI. Redefined AI. Local AI. ⚡🤖🛡️💻🔬