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import torch
from transformers import pipeline
import gradio as gr
# Init pipeline
pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
torch_dtype=torch.bfloat16, device_map="auto")
def predict(input_text):
# Formatting messages for the chatbot
messages = [
{
"role": "system",
"content": "You are a conversational text generation chatbot.",
},
{
"role": "user",
"content": "Hello! I am a chatbot designed to generate conversational text. How can I assist you today?",
}
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Create answer
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
# Return geberate text
return outputs[0]["generated_text"]
# Gradio Config
title = "Conversation style"
description = "Talk to a chatbot that responds like a conversational chat."
examples = [["¿How are you"]]
iface = gr.Interface(
fn=predict,
title=title,
description=description,
examples=examples,
inputs=gr.Textbox(label="Your message"),
outputs=gr.Textbox(label="Answer Chatbot"),
).launch()
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