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Running on Zero
Running on Zero
| 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}" | |
| 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 | |