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@ -5,6 +5,7 @@ from modal import App, Volume, Image
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app = modal.App("pricer-service") |
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image = Image.debian_slim().pip_install("huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft") |
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image.add_local_python_source("hello", "llama") #CP: adding here based on Deprecation warnings... |
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secrets = [modal.Secret.from_name("hf-secret")] |
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# Constants |
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@ -12,31 +13,32 @@ secrets = [modal.Secret.from_name("hf-secret")]
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GPU = "T4" |
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B" |
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PROJECT_NAME = "pricer" |
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HF_USER = "ed-donner" # your HF name here! Or use mine if you just want to reproduce my results. |
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RUN_NAME = "2024-09-13_13.04.39" |
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HF_USER = "cproSD" # your HF name here! Or use mine if you just want to reproduce my results. |
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RUN_NAME = "2025-04-08_21.52.37" |
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PROJECT_RUN_NAME = f"{PROJECT_NAME}-{RUN_NAME}" |
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REVISION = "e8d637df551603dc86cd7a1598a8f44af4d7ae36" |
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FINETUNED_MODEL = f"{HF_USER}/{PROJECT_RUN_NAME}" |
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MODEL_DIR = "hf-cache/" |
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BASE_DIR = MODEL_DIR + BASE_MODEL |
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FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL |
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#DEL: MODEL_DIR = "/models/" |
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#DEL: BASE_DIR = MODEL_DIR + BASE_MODEL |
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#DEL: FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL |
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CACHE_DIR = "/cache" #CP: Use the standard /cache path for hf-hub-cache |
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QUESTION = "How much does this cost to the nearest dollar?" |
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PREFIX = "Price is $" |
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@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800) |
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#CP: Use the pre-configured hf-hub-cache Volume... |
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hf_cache_volume = Volume.from_name("hf-hub-cache") |
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@app.cls( |
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image=image.env({"HF_HUB_CACHE": CACHE_DIR}), |
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secrets=secrets, |
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gpu=GPU, |
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timeout=1800, |
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volumes={CACHE_DIR: hf_cache_volume} |
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) |
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class Pricer: |
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@modal.build() |
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def download_model_to_folder(self): |
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from huggingface_hub import snapshot_download |
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import os |
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os.makedirs(MODEL_DIR, exist_ok=True) |
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snapshot_download(BASE_MODEL, local_dir=BASE_DIR) |
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snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR) |
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@modal.enter() |
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def setup(self): |
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import os |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed |
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from peft import PeftModel |
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@ -50,18 +52,15 @@ class Pricer:
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) |
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# Load model and tokenizer |
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self.tokenizer = AutoTokenizer.from_pretrained(BASE_DIR) |
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self.tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) |
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self.tokenizer.pad_token = self.tokenizer.eos_token |
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self.tokenizer.padding_side = "right" |
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self.base_model = AutoModelForCausalLM.from_pretrained( |
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BASE_DIR, |
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BASE_MODEL, |
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quantization_config=quant_config, |
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device_map="auto" |
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) |
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self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION) |
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self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_MODEL) |
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@modal.method() |
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def price(self, description: str) -> float: |
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@ -86,4 +85,3 @@ class Pricer:
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@modal.method() |
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def wake_up(self) -> str: |
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return "ok" |
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