1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4tokenizer = AutoTokenizer.from_pretrained("ghosthets/Dex")
5model = AutoModelForCausalLM.from_pretrained("ghosthets/Dex")
6device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
7model.to(device)
8
9def ask_dex(prompt):
10 input_text = f"User: {prompt}\nDex:"
11 inputs = tokenizer(input_text, return_tensors="pt").to(device)
12 outputs = model.generate(
13 inputs.input_ids,
14 attention_mask=inputs.attention_mask,
15 max_length=256,
16 do_sample=True,
17 top_k=40,
18 top_p=0.92,
19 temperature=0.7,
20 pad_token_id=tokenizer.eos_token_id
21 )
22 return tokenizer.decode(outputs[0], skip_special_tokens=True).split("Dex:")[-1].strip()
23
24print(ask_dex("How to identify an XSS vulnerability?"))
Despite its small size, Dex delivers powerful outputs and will soon be trained on more curated data, making it sharper, smarter, and more secure.
