Upload pricer_service2.py with huggingface_hub

pricer_service2.py

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+import modal
+from modal import Volume, Image
+# Setup - define our infrastructure with code!
+
+app = modal.App("pricer-service")
+image = Image.debian_slim().pip_install(
+    "huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft"
+)
+
+# This collects the secret from Modal.
+# Depending on your Modal configuration, you may need to replace "huggingface-secret" with "hf-secret"
+secrets = [modal.Secret.from_name("huggingface-secret")]
+
+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}"
+CACHE_DIR = "/cache"
+
+# Change this to 1 if you want Modal to be always running, otherwise it will go cold after 2 mins
+MIN_CONTAINERS = 0
+
+PREFIX = "Price is $"
+QUESTION = "What does this cost to the nearest dollar?"
+
+hf_cache_volume = Volume.from_name("hf-hub-cache", create_if_missing=True)
+
+
+@app.cls(
+    image=image.env({"HF_HUB_CACHE": CACHE_DIR}),
+    secrets=secrets,
+    gpu=GPU,
+    timeout=1800,
+    min_containers=MIN_CONTAINERS,
+    volumes={CACHE_DIR: hf_cache_volume},
+)
+class Pricer:
+    @modal.enter()
+    def setup(self):
+        import torch
+        from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+        from peft import PeftModel
+
+        # 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
+        self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        self.tokenizer.padding_side = "right"
+        self.base_model = AutoModelForCausalLM.from_pretrained(
+            BASE_MODEL, quantization_config=quant_config, device_map="auto"
+        )
+        self.fine_tuned_model = PeftModel.from_pretrained(
+            self.base_model, FINETUNED_MODEL, revision=REVISION
+        )
+
+    @modal.method()
+    def price(self, description: str) -> float:
+        import re
+        import 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")
+        with torch.no_grad():
+            outputs = self.fine_tuned_model.generate(inputs, max_new_tokens=5)
+        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