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Model: galahad-mamad/GambronAI-Persian-Cybersecurity-r1 Source: Original Platform
2026-07-24 05:26:09 +08:00
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+---
+license: apache-2.0
+tags:
+- cybersecurity
+- offensive-security
+- defensive-security
+- hacking
+- exploits
+- persian
+- reasoning
+- r1
+languages:
+- fa
+- en
+pipeline_tag: text-generation
+---
+
+# GambronAI-Persian-Cybersecurity-r1 🏹
+
+**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")