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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, pipeline
from threading import Thread
# The huggingface model id for Microsoft's phi-2 model
checkpoint = "microsoft/phi-2"
# Download and load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.float32, device_map="auto", trust_remote_code=True)
# Streamer
streamer = TextIteratorStreamer(tokenizer=tokenizer, skip_prompt=True)
# Text generation pipeline
phi2 = pipeline(
"text-generation",
tokenizer=tokenizer,
model=model,
streamer=streamer,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
device_map="auto"
)
# Function that accepts a prompt and generates text using the phi2 pipeline
def generate(prompt, chat_history, max_new_tokens):
instruction = "You are a helpful assistant to 'User'. You do not respond as 'User' or pretend to be 'User'. You only respond once as 'Assistant'."
final_prompt = f"Instruction: {instruction}\n"
for sent, received in chat_history:
final_prompt += "User: " + sent + "\n"
final_prompt += "Assistant: " + received + "\n"
final_prompt += "User: " + prompt + "\n"
final_prompt += "Output:"
thread = Thread(target=phi2, kwargs={"text_inputs":final_prompt, "max_new_tokens":max_new_tokens})
thread.start()
generated_text = ""
chat_history.append((prompt, ""))
for word in streamer:
generated_text += word
response = generated_text.strip()
if "User:" in response:
response = response.split("User:")[0].strip()
if "Assistant:" in response:
response = response.split("Assistant:")[1].strip()
chat_history.pop()
chat_history.append((prompt, response))
yield "", chat_history
# Chat interface with gradio
with gr.Blocks() as demo:
gr.Markdown("""
# Phi-2 Chatbot Demo
This chatbot was created using Microsoft's 2.7 billion parameter [phi-2](https://huggingface.co/microsoft/phi-2) Transformer model.
In order to reduce the response time on this hardware, `max_new_tokens` has been set to `32` in the text generation pipeline. With this default configuration, it takes approximately `60 seconds` for each response to be generated. Use the slider below to increase or decrease the length of the generated text.
""")
tokens_slider = gr.Slider(8, 128, value=32, label="Maximum new tokens", info="A larger `max_new_tokens` parameter value gives you longer text responses but at the cost of a slower response time.")
chatbot = gr.Chatbot(label="Phi-2 Chatbot")
msg = gr.Textbox(label="Message", placeholder="Enter text here")
with gr.Row():
with gr.Column():
btn = gr.Button("Submit")
with gr.Column():
clear = gr.ClearButton([msg, chatbot])
btn.click(fn=generate, inputs=[msg, chatbot, tokens_slider], outputs=[msg, chatbot])
examples = gr.Examples(examples=["Who is Leonhard Euler?"], inputs=[msg])
demo.queue().launch() |