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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
# Load the base model and tokenizer
base_model_name = "microsoft/phi-2"
adapter_path = "./output" # Path to your trained LoRA adapter
def load_model():
print("Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
# Load the LoRA adapter
model = PeftModel.from_pretrained(model, adapter_path)
return model, tokenizer
# Load the model and tokenizer
model, tokenizer = load_model()
def generate_response(prompt, max_length=512, temperature=0.7, top_p=0.9):
"""Generate a response using the fine-tuned model."""
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
inputs = {k: v.to(model.device) for k, v in inputs.items()}
# Generate response
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Remove the prompt from the response
if response.startswith(prompt):
response = response[len(prompt):].strip()
return response
# Create the Gradio interface
demo = gr.Interface(
fn=generate_response,
inputs=[
gr.Textbox(label="Enter your prompt", lines=4),
gr.Slider(minimum=64, maximum=1024, value=512, label="Max Length"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.9, label="Top P"),
],
outputs=gr.Textbox(label="Generated Response", lines=8),
title="Phi-2 QLoRA Fine-tuned Assistant",
description="Enter a prompt to generate a response using the fine-tuned Phi-2 model.",
examples=[
["Write a Python function to calculate the factorial of a number"],
["Explain the concept of machine learning in simple terms"],
["Write a professional email requesting a meeting with a client"],
]
)
if __name__ == "__main__":
demo.launch(share=True) |