diff --git a/week8/pricer_service2.py b/week8/pricer_service2.py index 16d276b..1f46dc4 100644 --- a/week8/pricer_service2.py +++ b/week8/pricer_service2.py @@ -5,6 +5,7 @@ from modal import App, Volume, Image app = modal.App("pricer-service") image = Image.debian_slim().pip_install("huggingface", "torch", "transformers", "bitsandbytes", "accelerate", "peft") +image.add_local_python_source("hello", "llama") secrets = [modal.Secret.from_name("hf-secret")] # Constants @@ -17,26 +18,25 @@ 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}" -MODEL_DIR = "hf-cache/" -BASE_DIR = MODEL_DIR + BASE_MODEL -FINETUNED_DIR = MODEL_DIR + FINETUNED_MODEL +CACHE_DIR = "/cache" QUESTION = "How much does this cost to the nearest dollar?" PREFIX = "Price is $" -@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800) +#NOTE: Use the pre-configured hf-hub-cache Volume... +hf_cache_volume = Volume.from_name("hf-hub-cache") + +@app.cls( + image=image.env({"HF_HUB_CACHE": CACHE_DIR}), + secrets=secrets, + gpu=GPU, + timeout=1800, + volumes={CACHE_DIR: hf_cache_volume} +) class Pricer: - @modal.build() - def download_model_to_folder(self): - from huggingface_hub import snapshot_download - import os - os.makedirs(MODEL_DIR, exist_ok=True) - snapshot_download(BASE_MODEL, local_dir=BASE_DIR) - snapshot_download(FINETUNED_MODEL, revision=REVISION, local_dir=FINETUNED_DIR) @modal.enter() def setup(self): - import os import torch from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, set_seed from peft import PeftModel @@ -48,20 +48,17 @@ class Pricer: bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4" ) - + # Load model and tokenizer - - self.tokenizer = AutoTokenizer.from_pretrained(BASE_DIR) + 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_DIR, + BASE_MODEL, quantization_config=quant_config, device_map="auto" ) - - self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_DIR, revision=REVISION) + self.fine_tuned_model = PeftModel.from_pretrained(self.base_model, FINETUNED_MODEL, revision=REVISION) @modal.method() def price(self, description: str) -> float: @@ -85,5 +82,4 @@ class Pricer: @modal.method() def wake_up(self) -> str: - return "ok" - + return "ok" \ No newline at end of file