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import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = f"danielsteinigen/GenerAd-AI"
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(utterance):
batch = tokenizer(
f"User: {utterance}\n### What did the user talk about?\nOptions: hotel, restaurant, taxi, attraction, hospital, police, train\n\n### Domain:\n",
return_tensors="pt",
)
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch, max_new_tokens=2)
return tokenizer.decode(output_tokens[0], skip_special_tokens=True)
if __name__ == "__main__":
# make a gradio interface
import gradio as gr
gr.Interface(
make_inference,
[
gr.inputs.Textbox(lines=2, label="Utterance"),
],
gr.outputs.Textbox(label="Domain"),
title="Domain Detection",
description="Generative model that detects domains of utterances.",
).launch() |