84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
import modal
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from modal import App, Volume, Image
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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", "hf-xet")
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# This collects the secret from Modal.
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# Depending on your Modal configuration, you may need to replace "hf-secret" with "huggingface-secret"
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secrets = [modal.Secret.from_name("hf-secret")]
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# Constants
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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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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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CACHE_DIR = "/cache"
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# Change this to 1 if you want Modal to be always running, otherwise it will go cold after 2 mins
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MIN_CONTAINERS = 0
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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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hf_cache_volume = Volume.from_name("hf-hub-cache", create_if_missing=True)
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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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min_containers=MIN_CONTAINERS,
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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.enter()
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def setup(self):
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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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# Quant Config
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quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_use_double_quant=True,
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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_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_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_MODEL, revision=REVISION)
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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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set_seed(42)
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prompt = f"{QUESTION}\n\n{description}\n\n{PREFIX}"
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inputs = self.tokenizer.encode(prompt, return_tensors="pt").to("cuda")
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attention_mask = torch.ones(inputs.shape, device="cuda")
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outputs = self.fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1)
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result = self.tokenizer.decode(outputs[0])
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contents = result.split("Price is $")[1]
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contents = contents.replace(',','')
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match = re.search(r"[-+]?\d*\.\d+|\d+", contents)
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return float(match.group()) if match else 0 |