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@ -0,0 +1,47 @@ |
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# Run Continuous Integration (CI) Tests on Modal |
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|
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Note! |
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The HF secret in Modal is named "huggingface-secret". Pls rename if your secret has another name. |
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|
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## Test modal deployment |
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You can test pricer.ci in Modal: |
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(`modal deploy -m pricer.ci`) |
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In python CLI: |
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(`import modal`) |
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(`Pricer = modal.Cls.lookup("pricer-ci-testing", "Pricer")`) |
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(`pricer = Pricer()`) |
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(`reply = pricer.price.remote("Quadcast HyperX condenser mic, connects via usb-c to your computer for crystal clear audio")`) |
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(`print(reply)`) |
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|
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## Unit testing |
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Unit test strategy created like in |
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[This example repo](https://github.com/modal-labs/ci-on-modal) |
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|
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## Usage |
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All commands below are run from the root of the repository (this directory). |
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|
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### Run tests remotely on Modal |
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```bash |
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modal run pricer.ci |
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``` |
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On the first execution, the [container image](https://modal.com/docs/guide/custom-container) |
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for your application will be built. |
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This image will be cached on Modal and only rebuilt if one of its dependencies, |
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like the `requirements.txt` file, changes. |
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### Debug tests running remotely |
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To debug the tests, you can open a shell |
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in the exact same environment that the tests are run in: |
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|
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```bash |
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modal shell pricer.ci |
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``` |
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|
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_Note_: On the Modal worker, the `pytest` command is run from the home directory, `/root`, |
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which contains the `tests` folder, but the `modal shell` command will |
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drop you at the top of the filesystem, `/`. |
@ -0,0 +1,103 @@ |
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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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|
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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" |
@ -0,0 +1,101 @@ |
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from typing import Optional |
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from transformers import AutoTokenizer |
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import re |
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BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B" |
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MIN_TOKENS = 150 |
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MAX_TOKENS = 160 |
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MIN_CHARS = 300 |
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CEILING_CHARS = MAX_TOKENS * 7 |
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class Item: |
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""" |
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An Item is a cleaned, curated datapoint of a Product with a Price |
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""" |
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tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True) |
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PREFIX = "Price is $" |
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QUESTION = "How much does this cost to the nearest dollar?" |
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REMOVALS = ['"Batteries Included?": "No"', '"Batteries Included?": "Yes"', '"Batteries Required?": "No"', '"Batteries Required?": "Yes"', "By Manufacturer", "Item", "Date First", "Package", ":", "Number of", "Best Sellers", "Number", "Product "] |
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title: str |
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price: float |
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category: str |
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token_count: int = 0 |
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details: Optional[str] |
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prompt: Optional[str] = None |
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include = False |
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def __init__(self, data, price): |
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self.title = data['title'] |
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self.price = price |
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self.parse(data) |
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def scrub_details(self): |
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""" |
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Clean up the details string by removing common text that doesn't add value |
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""" |
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details = self.details |
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for remove in self.REMOVALS: |
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details = details.replace(remove, "") |
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return details |
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def scrub(self, stuff): |
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""" |
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Clean up the provided text by removing unnecessary characters and whitespace |
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Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers |
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""" |
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stuff = re.sub(r'[:\[\]"{}【】\s]+', ' ', stuff).strip() |
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stuff = stuff.replace(" ,", ",").replace(",,,",",").replace(",,",",") |
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words = stuff.split(' ') |
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select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)] |
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return " ".join(select) |
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def parse(self, data): |
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""" |
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Parse this datapoint and if it fits within the allowed Token range, |
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then set include to True |
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""" |
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contents = '\n'.join(data['description']) |
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if contents: |
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contents += '\n' |
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features = '\n'.join(data['features']) |
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if features: |
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contents += features + '\n' |
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self.details = data['details'] |
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if self.details: |
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contents += self.scrub_details() + '\n' |
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if len(contents) > MIN_CHARS: |
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contents = contents[:CEILING_CHARS] |
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text = f"{self.scrub(self.title)}\n{self.scrub(contents)}" |
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tokens = self.tokenizer.encode(text, add_special_tokens=False) |
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if len(tokens) > MIN_TOKENS: |
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tokens = tokens[:MAX_TOKENS] |
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text = self.tokenizer.decode(tokens) |
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self.make_prompt(text) |
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self.include = True |
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def make_prompt(self, text): |
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""" |
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Set the prompt instance variable to be a prompt appropriate for training |
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""" |
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self.prompt = f"{self.QUESTION}\n\n{text}\n\n" |
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self.prompt += f"{self.PREFIX}{str(round(self.price))}.00" |
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self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False)) |
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def test_prompt(self): |
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""" |
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Return a prompt suitable for testing, with the actual price removed |
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""" |
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return self.prompt.split(self.PREFIX)[0] + self.PREFIX |
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def __repr__(self): |
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""" |
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Return a String version of this Item |
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""" |
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return f"<{self.title} = ${self.price}>" |
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@ -0,0 +1,10 @@ |
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import time |
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import modal |
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from datetime import datetime |
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Pricer = modal.Cls.lookup("pricer-service", "Pricer") |
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pricer = Pricer() |
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while True: |
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reply = pricer.wake_up.remote() |
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print(f"{datetime.now()}: {reply}") |
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time.sleep(30) |
@ -0,0 +1,44 @@ |
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import modal |
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from modal import App, Volume, Image |
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# Setup |
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app = modal.App("llama") |
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image = Image.debian_slim().pip_install("torch", "transformers", "bitsandbytes", "accelerate") |
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secrets = [modal.Secret.from_name("hf-secret")] |
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GPU = "T4" |
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MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B" # "google/gemma-2-2b" |
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@app.function(image=image, secrets=secrets, gpu=GPU, timeout=1800) |
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def generate(prompt: str) -> str: |
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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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|
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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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|
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = "right" |
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|
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL_NAME, |
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quantization_config=quant_config, |
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device_map="auto" |
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) |
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set_seed(42) |
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inputs = 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 = model.generate(inputs, attention_mask=attention_mask, max_new_tokens=5, num_return_sequences=1) |
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return tokenizer.decode(outputs[0]) |
@ -0,0 +1,75 @@ |
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import math |
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import matplotlib.pyplot as plt |
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GREEN = "\033[92m" |
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YELLOW = "\033[93m" |
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RED = "\033[91m" |
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RESET = "\033[0m" |
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COLOR_MAP = {"red":RED, "orange": YELLOW, "green": GREEN} |
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class Tester: |
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def __init__(self, predictor, data, title=None, size=250): |
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self.predictor = predictor |
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self.data = data |
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self.title = title or predictor.__name__.replace("_", " ").title() |
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self.size = size |
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self.guesses = [] |
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self.truths = [] |
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self.errors = [] |
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self.sles = [] |
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self.colors = [] |
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def color_for(self, error, truth): |
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if error<40 or error/truth < 0.2: |
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return "green" |
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elif error<80 or error/truth < 0.4: |
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return "orange" |
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else: |
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return "red" |
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|
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def run_datapoint(self, i): |
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datapoint = self.data[i] |
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guess = self.predictor(datapoint) |
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truth = datapoint.price |
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error = abs(guess - truth) |
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log_error = math.log(truth+1) - math.log(guess+1) |
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sle = log_error ** 2 |
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color = self.color_for(error, truth) |
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title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+"..." |
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self.guesses.append(guess) |
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self.truths.append(truth) |
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self.errors.append(error) |
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self.sles.append(sle) |
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self.colors.append(color) |
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print(f"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}") |
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|
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def chart(self, title): |
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max_error = max(self.errors) |
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plt.figure(figsize=(12, 8)) |
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max_val = max(max(self.truths), max(self.guesses)) |
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plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6) |
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plt.scatter(self.truths, self.guesses, s=3, c=self.colors) |
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plt.xlabel('Ground Truth') |
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plt.ylabel('Model Estimate') |
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plt.xlim(0, max_val) |
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plt.ylim(0, max_val) |
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plt.title(title) |
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plt.show() |
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|
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def report(self): |
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average_error = sum(self.errors) / self.size |
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rmsle = math.sqrt(sum(self.sles) / self.size) |
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hits = sum(1 for color in self.colors if color=="green") |
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title = f"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%" |
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self.chart(title) |
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def run(self): |
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self.error = 0 |
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for i in range(self.size): |
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self.run_datapoint(i) |
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self.report() |
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|
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@classmethod |
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def test(cls, function, data): |
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cls(function, data).run() |
@ -0,0 +1,6 @@ |
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|
huggingface |
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torch |
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transformers |
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bitsandbytes |
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|
accelerate |
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|
peft |
Loading…
Reference in new issue