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@ -1,15 +1,22 @@
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import modal |
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from modal import App, Volume, Image |
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from pathlib import PurePosixPath |
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# Setup - define our infrastructure with code! |
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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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secrets = [modal.Secret.from_name("hf-secret")] |
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secrets = [modal.Secret.from_name("huggingface-secret")] |
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image = modal.Image.debian_slim().pip_install( |
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"huggingface", "torch", "transformers", "bitsandbytes", |
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"accelerate", "peft", "huggingface_hub[hf_transfer]" |
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).env({"HF_HUB_ENABLE_HF_TRANSFER": "1"}) |
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# Constants |
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# This is where we cache model files to avoid redownloading each time a container is started |
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hf_cache_vol = modal.Volume.from_name("hf-cache", create_if_missing=True) |
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GPU = "T4" |
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# Keep N containers active to avoid cold starts |
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MIN_CONTAINERS = 0 |
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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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@ -17,30 +24,28 @@ RUN_NAME = "2024-09-13_13.04.39"
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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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# Mount for cache location |
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MODEL_DIR = PurePosixPath("/models") |
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BASE_DIR = MODEL_DIR / BASE_MODEL |
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FINETUNED_DIR = MODEL_DIR / FINETUNED_MODEL |
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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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@app.cls(image=image, secrets=secrets, gpu=GPU, timeout=1800, min_containers=MIN_CONTAINERS, volumes={MODEL_DIR: hf_cache_vol}) |
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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 huggingface_hub import snapshot_download |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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from peft import PeftModel |
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# Download and cache model files to the volume |
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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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# Quant Config |
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quant_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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@ -48,9 +53,8 @@ class Pricer:
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bnb_4bit_compute_dtype=torch.bfloat16, |
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bnb_4bit_quant_type="nf4" |
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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.pad_token = self.tokenizer.eos_token |
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self.tokenizer.padding_side = "right" |
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@ -65,11 +69,8 @@ class Pricer:
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@modal.method() |
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def price(self, description: str) -> float: |
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import os |
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import re |
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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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import re, torch |
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from transformers import set_seed |
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set_seed(42) |
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prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}" |
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@ -83,7 +84,3 @@ class Pricer:
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match = re.search(r"[-+]?\d*\.\d+|\d+", contents) |
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return float(match.group()) if match else 0 |
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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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