Math_AI / app.py
JuliaUpton's picture
Update app.py
a67e26b verified
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "JuliaUpton/Math_AI"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
merged_model = PeftModel.from_pretrained(model, peft_model_id)
def input_from_text(instruction):
return f"<s>[INST]Below is a math inquiry, please answer it as a math expert showing your thought process.\n\n### Inquiry:\n{instruction}\n\n### Response:[/INST]"
def make_inference(instruction):
inputs = mixtral_tokenizer(input_from_text(instruction), return_tensors="pt")
outputs = merged_model.generate(
**inputs,
max_new_tokens=150,
generation_kwargs={"repetition_penalty" : 1.7}
)
# print(mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True))
result = mixtral_tokenizer.decode(outputs[0], skip_special_tokens=True).split("[/INST]")[1]
return result
if __name__ == "__main__":
# make a gradio interface
import gradio as gr
gr.Interface(
make_inference,
[
gr.Textbox(lines=5, label="Instruction"),
],
gr.Textbox(label="Answer"),
title="Math-AI",
description="Math-AI is a generative model that answers math questions",
).launch()