Chris Alexiuk
Update app.py
faa0b42
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = f"c-s-ale/AI-Superstar-Model"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
load_in_8bit=True,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)
def make_inference(question):
batch = tokenizer(
f"Please answer the following question to the best of your ability.\n\n### Question:\n{question}\n### Answer:\n",
return_tensors="pt",
)
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=70)
return tokenizer.decode(output_tokens[0], skip_special_tokens=True).split("\n\n")[-1]
if __name__ == "__main__":
# make a gradio interface
import gradio as gr
gr.Interface(
make_inference,
[
gr.inputs.Textbox(lines=2, label="Question"),
],
gr.outputs.Textbox(label="Answer"),
title="AI Super Star",
description="AI Super Star is a tool you can use to guide you on your ML journey.",
).launch()