Upload pricer_ephemeral.py with huggingface_hub

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