import modal from pathlib import PurePosixPath # Setup - define our infrastructure with code! app = modal.App("pricer-service") secrets = [modal.Secret.from_name("huggingface-secret")] image = modal.Image.debian_slim().pip_install( "huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft", "huggingface_hub[hf_transfer]" ).env({"HF_HUB_ENABLE_HF_TRANSFER": "1"}) # This is where we cache model files to avoid redownloading each time a container is started hf_cache_vol = modal.Volume.from_name("hf-cache", create_if_missing=True) GPU = "T4" # Keep N containers active to avoid cold starts MIN_CONTAINERS = 0 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}" # Mount for cache location MODEL_DIR = PurePosixPath("/models") 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 $" @app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800, min_containers=MIN_CONTAINERS, volumes={MODEL_DIR: hf_cache_vol}) class Pricer: @modal.enter() def setup(self): import torch from huggingface_hub import snapshot_download from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel # Download and cache model files to the volume snapshot_download(BASE_MODEL, local_dir=BASE_DIR) snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR) # 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) @modal.method() def price(self, description: str) -> float: import re, torch from transformers import set_seed 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