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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
# Load model directly
tokenizer = AutoTokenizer.from_pretrained("MTSAIR/multi_verse_model")
model = AutoModelForCausalLM.from_pretrained("MTSAIR/multi_verse_model")
def greet(name):
#i want to get same result res = pipe(name, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
#but using tokenizer and model
input_ids = tokenizer.encode(name, return_tensors='pt')
res = model.generate(input_ids, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
generated = tokenizer.decode(res[0], skip_special_tokens=True)
return generated
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
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