from pathlib import Path import modal ROOT_PATH = Path(__file__).parent.parent image = ( modal.Image.debian_slim() .pip_install("pytest") .pip_install_from_requirements(ROOT_PATH / "requirements.txt") ) app = modal.App("pricer-ci-testing", image=image) # mount: add local files to the remote container tests = modal.Mount.from_local_dir(ROOT_PATH / "tests", remote_path="/root/tests") @app.function(gpu="any", mounts=[tests]) def pytest(): import subprocess subprocess.run(["pytest", "-vs"], check=True, cwd="/root") secrets = [modal.Secret.from_name("huggingface-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}" MODEL_DIR = "hf-cache/" BASE_DIR = MODEL_DIR + BASE_MODEL FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL QUESTION = "How much does this cost to the nearest dollar?" PREFIX = "Price is $" class Pricer: def download_model_to_folder(self): from huggingface_hub import snapshot_download import os os.makedirs(MODEL_DIR, exist_ok=True) print(f"Using this HF Token: {hf_token}") snapshot_download(BASE_MODEL, local_dir=BASE_DIR, use_auth_token=hf_token) snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR, use_auth_token=hf_token) def setup(self): import os import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed from peft import PeftModel # 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 self.tokenizer = AutoTokenizer.from_pretrained(BASE_DIR) self.tokenizer.pad_token = self.tokenizer.eos_token self.tokenizer.padding_side = "right" self.base_model = AutoModelForCausalLM.from_pretrained( BASE_DIR, quantization_config=quant_config, device_map="auto" ) self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION) def price(self, description: str) -> float: import os import re import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed from peft import PeftModel set_seed(42) prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}" inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda") attention_mask = torch.ones(inputs.shape, device="cuda") outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1) result = self.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 def wake_up(self) -> str: return "ok"