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import gradio as gr
import torch
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
TextIteratorStreamer,
pipeline,
AutoConfig,
)
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="cpu", trust_remote_code=True
)
#model_name_or_path = "TheBloke/phi-2-GPTQ"
## To use a different branch, change revision
## For example: revision="gptq-4bit-32g-actorder_True"
#config = AutoConfig.from_pretrained(model_name_or_path,trust_remote_code=True)
#config.quantization_config["use_exllama"] = False
#model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
# device_map="cpu",
# trust_remote_code=True,
# revision="main",
# config=config)
#tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
# 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'."
instruction = "You are a helpful assistant to 'User'. You will answer any question for 'User'."
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 Chatbot Demo
This chatbot was created using TheBloke/phi-2-GPTQ from 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, set `max_new_tokens` to lower number in the text generation pipeline.
"""
)
tokens_slider = gr.Slider(
8,
128,
value=128,
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()
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