From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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103 lines
3.4 KiB
103 lines
3.4 KiB
from pathlib import Path |
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import modal |
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ROOT_PATH = Path(__file__).parent.parent |
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image = ( |
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modal.Image.debian_slim() |
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.pip_install("pytest") |
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.pip_install_from_requirements(ROOT_PATH / "requirements.txt") |
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) |
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app = modal.App("pricer-ci-testing", image=image) |
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# mount: add local files to the remote container |
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tests = modal.Mount.from_local_dir(ROOT_PATH / "tests", remote_path="/root/tests") |
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@app.function(gpu="any", mounts=[tests]) |
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def pytest(): |
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import subprocess |
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subprocess.run(["pytest", "-vs"], check=True, cwd="/root") |
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secrets = [modal.Secret.from_name("huggingface-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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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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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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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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# 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_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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self.base_model = AutoModelForCausalLM.from_pretrained( |
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BASE_DIR, |
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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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@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 |
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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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