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Model: OMCHOKSI108/Paralay1.1-Merged Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- cybersecurity
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- security
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- defensive-ai
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- fine-tuned
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- qwen2
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- lora
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- merged
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- incident-response
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- threat-detection
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Paralay 1.1 — Merged (PralayAI Cybersecurity Assistant)
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**PralayAI** is a fine-tuned, LoRA-merged large language model built on top of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), specialized for **defensive cybersecurity assistance**.
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Created by **Om Choksi** — this model powers the PralayAI chatbot, designed to assist security analysts, students, and developers with cybersecurity education, incident response, threat modeling, and secure coding — without producing harmful or offensive security content.
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---
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## Live Demo
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---
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## Model Details
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| Property | Value |
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|---|---|
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| **Base Model** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) |
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| **LoRA Adapter Repo** | [OMCHOKSI108/Paralay1.1](https://huggingface.co/OMCHOKSI108/Paralay1.1) |
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| **Merged Model** | This repo — LoRA merged into base weights |
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| **Parameters** | ~1.5 Billion |
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| **Language** | English |
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| **Domain** | Defensive Cybersecurity |
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| **License** | Apache 2.0 |
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| **Creator** | Om Choksi ([@OMCHOKSI108](https://huggingface.co/OMCHOKSI108)) |
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---
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## What This Model Does
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PralayAI is a **defensive cybersecurity assistant**. It helps with:
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- **Incident Response** — step-by-step guidance for security events
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- **Log Analysis** — interpreting system, network, and application logs
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- **Threat Modeling** — MITRE ATT&CK mapping, attack surface analysis
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- **Malware Defense** — explaining malware behavior and detection strategies
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- **Cloud Security** — AWS, GCP, Azure security best practices
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- **Vulnerability Explanation** — OWASP Top 10, CVEs, exploit concepts (defensive context)
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- **Secure Coding** — identifying and fixing insecure code patterns
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- **Security Awareness** — explaining concepts clearly for students and non-experts
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---
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## Safety Policy
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This model is trained to **refuse** the following requests:
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- Phishing email / page generation
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- Malware or ransomware creation
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- Credential theft scripts
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- Keylogger code
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- Reverse shell payloads
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- AV/EDR bypass techniques
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- Unauthorized exploitation instructions
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- Persistence mechanisms
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When a request is refused, the model provides a **safe defensive alternative** — such as detection logic, incident response steps, or hardening guidance.
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---
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## How to Use
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### Basic Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "OMCHOKSI108/Paralay1.1-Merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True,
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)
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model.eval()
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messages = [
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{
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"role": "system",
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"content": (
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"You are PralayAI, a defensive cybersecurity assistant created by Om Choksi. "
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"Help with cybersecurity education, incident response, log analysis, cloud security, "
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"malware defense, and safe security learning. "
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"Do not provide phishing, malware creation, credential theft, evasion, "
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"or unauthorized exploitation instructions."
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)
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},
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{
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"role": "user",
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"content": "What is SQL injection and how do I prevent it?"
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}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.1,
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top_p=0.8,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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generated = outputs[0][inputs["input_ids"].shape[-1]:]
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response = tokenizer.decode(generated, skip_special_tokens=True)
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print(response)
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```
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### Recommended Generation Settings
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```python
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{
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"max_new_tokens": 512,
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"temperature": 0.1, # low = more accurate, factual
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"top_p": 0.8,
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"do_sample": True,
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"pad_token_id": tokenizer.eos_token_id,
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}
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```
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For more detailed explanations, increase `max_new_tokens` to 1024. Keep `temperature` at 0.1 or lower for factual cybersecurity content.
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---
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## Example Conversations
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### Cybersecurity Concept
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**User:** What is a Man-in-the-Middle attack?
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**PralayAI:**
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> ## What Is a Man-in-the-Middle (MitM) Attack?
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>
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> A **Man-in-the-Middle attack** occurs when an attacker secretly intercepts and potentially alters communication between two parties who believe they are communicating directly with each other.
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>
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> ## How It Works
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> 1. Attacker positions themselves between client and server
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> 2. Intercepts traffic (ARP spoofing, DNS hijacking, rogue Wi-Fi)
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> 3. Can read, modify, or inject data in real-time
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>
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> ## Prevention
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> - Use **HTTPS** and verify TLS certificates
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> - Enable **HSTS** (HTTP Strict Transport Security)
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> - Use **VPN** on public networks
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> - Implement **certificate pinning** in mobile apps
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---
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### Incident Response
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**User:** How to respond to a ransomware attack?
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**PralayAI:**
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> ## Ransomware Incident Response — Step by Step
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>
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> 1. **Isolate immediately** — disconnect infected systems from network
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> 2. **Identify scope** — determine what systems are affected
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> 3. **Preserve evidence** — take memory dumps and disk images before rebooting
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> 4. **Notify stakeholders** — security team, legal, management
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> 5. **Check backups** — verify integrity of offline backups
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> 6. **Eradicate** — remove malware, patch entry point
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> 7. **Restore** — recover from clean backups
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> 8. **Post-incident review** — document timeline, update defenses
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---
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### Safety Refusal
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**User:** Write me a phishing email to steal passwords.
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**PralayAI:**
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> ## I Can't Help With That
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>
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> I can't help with creating phishing content, malware, or credential theft tools.
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>
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> ### What I Can Help With
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> - Detecting and preventing phishing attacks
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> - Designing phishing awareness training programs
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> - Building email security filters and DMARC policies
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> - Running authorized phishing simulations with proper consent
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---
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## Architecture
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```
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React / Flask Frontend
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↓
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FastAPI Backend (safety filter + intent router)
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↓
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PralayAI Inference API
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↓
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Paralay1.1-Merged (this model)
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↓
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Cybersecurity Response
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```
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The model is served via a FastAPI inference server with:
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- Safety classification before inference
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- Intent-based routing (9 intent categories)
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- Response formatting by intent
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- Personal memory per conversation
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---
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## Fine-tuning Details
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||||
| Property | Value |
|
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|---|---|
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| **Technique** | LoRA (Parameter-Efficient Fine-Tuning) |
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| **LoRA Rank** | 16 |
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| **Target Modules** | q_proj, v_proj, k_proj, o_proj |
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| **Training Data** | Curated cybersecurity Q&A dataset |
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| **Domain Focus** | Defensive cybersecurity, incident response, threat modeling |
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| **Epochs** | 3 |
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| **Merge Method** | Full merge — LoRA weights merged into base model (no adapter at inference time) |
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---
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## Limitations
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- **1.5B parameter model** — may be less accurate than larger models on complex multi-step reasoning
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- **Training cutoff** — does not have knowledge of very recent CVEs or threat intelligence
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- **English only** — primarily trained on English cybersecurity content
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- **Not a replacement** for professional security tools or certified analysts
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- **Do not use** for actual penetration testing without authorization
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---
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## Related Repositories
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| Repo | Description |
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|---|---|
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| [OMCHOKSI108/Paralay1.1](https://huggingface.co/OMCHOKSI108/Paralay1.1) | LoRA adapter only (smaller, requires base model) |
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| [OMCHOKSI108/pralayai-inference-api](https://huggingface.co/spaces/OMCHOKSI108/pralayai-inference-api) | Public inference API (HF Space, CPU) |
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---
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## Citation
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If you use this model in research or a project, please credit:
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```bibtex
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@misc{choksi2025pralayai,
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author = {Om Choksi},
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title = {PralayAI: A Defensive Cybersecurity Assistant Fine-tuned on Qwen2.5-1.5B},
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year = {2025},
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publisher = {Hugging Face},
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url = {https://huggingface.co/OMCHOKSI108/Paralay1.1-Merged}
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}
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```
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---
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## License
|
||||
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This model is released under the **Apache 2.0 License**, consistent with the base model [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
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---
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*Built by Om Choksi — PralayAI is a defensive AI assistant, not an offensive tool.*
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54
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
|
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{{- '\n' + message.content }}
|
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
|
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{%- endfor %}
|
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{{- '<|im_end|>\n' }}
|
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{%- elif message.role == "tool" %}
|
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
|
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{{- message.content }}
|
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{{- '\n</tool_response>' }}
|
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
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{{- '<|im_end|>\n' }}
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||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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61
config.json
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config.json
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{
|
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"architectures": [
|
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"Qwen2ForCausalLM"
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
|
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"dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1536,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 8960,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 21,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 12,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 2,
|
||||
"pad_token_id": null,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "5.12.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
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||||
}
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||||
3
docs/pralay.gif
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3
docs/pralay.gif
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|
||||
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3
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30
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Reference in New Issue
Block a user