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
3.6 KiB
103 lines
3.6 KiB
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 # Any less than this, and we don't have enough useful content |
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MAX_TOKENS = 160 # Truncate after this many tokens. Then after adding in prompt text, we will get to around 180 tokens |
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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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