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# -*- coding: utf-8 -*-
"""Untitled12.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1goHcmXF0Gc4_X9PN-zecV77j9KeI6Dmn
"""
#!pip install -q gradio
#!pip install torch transformers accelerate einops
#!pip install peft
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
# Download and load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
#Download safetensor of adapter of fine-tune Phi-2 model
from peft import PeftModel, PeftConfig
config = PeftConfig.from_pretrained("mudogruer/phi-2-SciQ")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
model = PeftModel.from_pretrained(model, "mudogruer/phi-2-SciQ")
# Text generation pipeline
phi2 = pipeline(
"text-generation",
tokenizer=tokenizer,
model=model,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
device_map="cpu",
)
# Function that accepts a prompt and generates text using the phi2 pipeline
def generate(message, 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: " + message + "\n"
final_prompt += "Output:"
# if (
# len(tokenizer.tokenize(final_prompt))
# >= tokenizer.model_max_length - max_new_tokens
# ):
# final_prompt = "Instruction: Say 'Input exceeded context size, please clear the chat history and retry!' Output:"
# Streamer
streamer = TextIteratorStreamer(
tokenizer=tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=300.0
)
thread = Thread(
target=phi2,
kwargs={
"text_inputs": final_prompt,
"max_new_tokens": max_new_tokens,
"streamer": streamer,
},
)
thread.start()
generated_text = ""
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()
yield response
# Chat interface with gradio
with gr.Blocks() as demo:
gr.Markdown(
"""
# Phi-2 Scientific Question Chatbot
This chatbot was created using Microsoft's 2.7 billion parameter [phi-2](https://huggingface.co/microsoft/phi-2) Transformer model.
Phi-2 model was fine-tuned with questions including highschool level physics chemistry biology QA using SciQ dataset. In order to reduce the response time on this hardware, `max_new_tokens` has been set to `21` in the text generation pipeline. With this default configuration, it takes approximately `60 seconds` for the response to start being generated, and streamed one word at a time. Use the slider below to increase or decrease the length of the generated text.
For the safetensor: huggingface.co/mudogruer
"""
)
tokens_slider = gr.Slider(
8,
128,
value=21,
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.ChatInterface(
fn=generate,
additional_inputs=[tokens_slider],
stop_btn=None,
examples=[["Who is Leonhard Euler?"]],
)
demo.queue().launch() |