66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
import modal
|
|
from modal import App, Image
|
|
|
|
# Setup
|
|
|
|
app = modal.App("pricer")
|
|
image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate", "peft","hf-xet")
|
|
secrets = [modal.Secret.from_name("hf-secret")]
|
|
|
|
# Constants
|
|
|
|
GPU = "T4"
|
|
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
|
|
PROJECT_NAME = "pricer"
|
|
HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results.
|
|
RUN_NAME = "2024-09-13_13.04.39"
|
|
PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}"
|
|
REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36"
|
|
FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}"
|
|
|
|
|
|
@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800)
|
|
def price(description: str) -> float:
|
|
import os
|
|
import re
|
|
import torch
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed
|
|
from peft import PeftModel
|
|
|
|
QUESTION = "How much does this cost to the nearest dollar?"
|
|
PREFIX = "Price is $"
|
|
|
|
prompt = f"{QUESTION}\n{description}\n{PREFIX}"
|
|
|
|
# Quant Config
|
|
quant_config = BitsAndBytesConfig(
|
|
load_in_4bit=True,
|
|
bnb_4bit_use_double_quant=True,
|
|
bnb_4bit_compute_dtype=torch.bfloat16,
|
|
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")
|
|
attention_mask = torch.ones(inputs.shape, device="cuda")
|
|
outputs = fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
|
|
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 |