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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| model_name = "DeepHat/DeepHat-V1-7B" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| device_map="auto", | |
| low_cpu_mem_usage=True | |
| ) | |
| def chat(input_text): | |
| messages = [ | |
| {"role": "system", "content": "You are a cybersecurity expert."}, | |
| {"role": "user", "content": input_text} | |
| ] | |
| text = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True | |
| ) | |
| inputs = tokenizer(text, return_tensors="pt") | |
| outputs = model.generate(**inputs, max_new_tokens=120) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| gr.Interface(fn=chat, inputs="text", outputs="text").launch() |