GambronAI-Persian-Cybersecurity-r1 is an advanced, domain-specific large language model fine-tuned for tactical execution in cybersecurity operations, offensive security (hacking), exploit analysis, and reverse engineering. Powered by a native reasoning engine, the model dynamically dissects complex security challenges with high-fidelity performance in both Persian (Farsi) and English.

The architecture enforces a structured step-by-step analytical phase inside <think>...</think> blocks prior to rendering the final, uncensored technical execution or exploit payload.

Key Features 🚀

Cybersecurity Specialization: Deep conceptual understanding of network exploitation, web application security, malware analysis, cryptography, and modern DevSecOps vulnerabilities.
Native Reasoning Engine: Solves complex multi-staged security puzzles via systematic chain-of-thought processing to maximize root-cause analysis accuracy.
Bilingual Technical Fluency: Seamlessly bridges complex Western technical terminology with native Persian security reporting, documentation, and code generation.
Unrestricted Analysis: Optimized for rigorous security research, allowing penetration testing scenarios, vulnerability assessments, and proof-of-concepts to run completely unfiltered.

Model Loading & Inference Guide 💻

The custom chat structure is natively compiled within the model's tokenizer configs. End-users do not need to manually append, paste, or override the chat template. The system automatically initializes the reasoning framework during execution.

Python Implementation (Transformers)

Run the script below to initialize the model and start an interactive streaming session:

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "galahad-mamad/GambronAI-Persian-Cybersecurity-r1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)

print("\n" + "="*50)
print("GambronAI Core Engine Initialized Successfully.")
print("="*50 + "\n")

chat_history = []

while True:
    user_input = input("User: ")
    if user_input.strip().lower() in ['exit', 'quit']:
        break
        
    chat_history.append({"role": "user", "content": user_input})
    
    full_prompt = tokenizer.apply_chat_template(
        chat_history, 
        tokenize=False, 
        add_generation_prompt=True
    )
    
    inputs = tokenizer([full_prompt], return_tensors="pt").to("cuda")
    
    print("\nGambronAI:")
    print("-" * 30)
    
    with torch.no_grad():
        _ = model.generate(
            **inputs,
            max_new_tokens=1536,
            temperature=0.7,
            do_sample=True,
            top_p=0.95,
            streamer=streamer,
            pad_token_id=tokenizer.eos_token_id
        )
    print("\n" + "-" * 30 + "\n")