| import gradio as gr |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
| |
| MODEL_ID = "darkc0de/XortronCriminalComputingConfig" |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| model = AutoModelForCausalLM.from_pretrained(MODEL_ID) |
|
|
| def respond(message, history): |
| inputs = tokenizer(message, return_tensors="pt") |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=256, |
| do_sample=True, |
| temperature=0.7, |
| top_p=0.9, |
| ) |
| reply = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| return reply |
|
|
| demo = gr.ChatInterface( |
| fn=respond, |
| type="messages", |
| chatbot=gr.Chatbot(height=600, show_copy_button=True), |
| textbox=gr.Textbox(placeholder="Chat with Xortron...", container=False, scale=7), |
| title="Xortron Chat", |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|