Capstone / pricer_ephemeral.py
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import modal
from modal import Image
# Setup
app = modal.App("pricer")
image = Image.debian_slim().pip_install(
"torch", "transformers", "bitsandbytes", "accelerate", "peft"
)
secrets = [modal.Secret.from_name("huggingface-secret")]
# Constants
GPU = "T4"
BASE_MODEL = "meta-llama/Llama-3.2-3B"
PROJECT_NAME = "price"
HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
RUN_NAME = "2025-11-28_18.47.07"
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
REVISION = "b19c8bfea3b6ff62237fbb0a8da9779fc12cefbd"
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800)
def price(description: str) -> float:
import re
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
from peft import PeftModel
PREFIX = "Price is $"
QUESTION = "What does this cost to the nearest dollar?"
prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
# Quant Config
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
)
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
base_model = AutoModelForCausalLM.from_pretrained(
BASE_MODEL, quantization_config=quant_config, device_map="auto"
)
fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)
set_seed(42)
inputs = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = fine_tuned_model.generate(inputs, max_new_tokens=5)
result = tokenizer.decode(outputs[0])
contents = result.split("Price is $")[1]
contents = contents.replace(",", "")
match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
return float(match.group()) if match else 0