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import time
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-128k-instruct",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
def chat(message):
messages = [
{"role": "user", "content": "Hi"},
{"role": "assistant", "content": "Hello.. How may I help you?"},
{"role": "user", "content": message},
]
output = pipe(messages, **generation_args)
return output[0]['generated_text']
description = """
<div style="text-align: center;">
<h1>Phi-3-mini-128k-instruct</h1>
<p>This Q/A chatbot is based on the Phi-3-mini-128k-instruct model by Microsoft.</p>
<p>Feel free to ask any questions or start a conversation!</p>
</div>
"""
#demo = gr.ChatInterface(chat, description=description).queue()
demo= gr.Interface(fn=chat, inputs="textbox", outputs="textbox",description=description)
demo.launch()
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