app.py

import torch
import gradio as gr
from transformers import pipeline, logging, AutoModelForCausalLM, AutoTokenizer

model_name = "microsoft/phi-2"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True
)
model.config.use_cache = False

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token

peft_model_folder = './ckpts'
model.load_adapter(peft_model_folder)

def generate_text(input_text, max_length):
  pipe = pipeline(task="text-generation",model=model,tokenizer=tokenizer, max_length=max_length)
  result = pipe(f"<s>[INST] {input_text} [/INST]")
  return_answer = result[0]['generated_text']
  return return_answer

# Create a Gradio interface
title = "Phi2-QLora."
description = "A simple Gradio interface to demo Phi2 model finetuned on openassist dataset with Qlora."
examples = [["What is Large Language Model?"],
            ["Why Python is most popular Language?"],
            ["How to do rice?"]]
demo = gr.Interface(
    generate_text,
    inputs=[
        gr.TextArea(label="Enter Question"),
        gr.Slider(1, 200, value = 10, step=1, label="Max Length")
    ],

    outputs=[
        gr.Textbox(label="Response from Phi2 Model: "),
        gr.TextArea(label="Tokens")
    ],
    title=title,
    description=description,
    examples=examples,
    cache_examples=False,
    live=True
)
demo.launch()