from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base_model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen3-14B", torch_dtype="auto", device_map="auto")model = PeftModel.from_pretrained(base_model, "atlas-institute/qwen3-14b-orchestrator-dpo")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B") messages = [ {"role": "system", "content": "You are Nexus, an offensive security orchestrator agent."}, {"role": "user", "content": "Analyze the nmap results and prioritize exploitation targets."},]text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(text, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=768)print(tokenizer.decode(outputs[0], skip_special_tokens=True))