2521 lines
85 KiB
Plaintext
2521 lines
85 KiB
Plaintext
{
|
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"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "41fb78a4-5aa1-4288-9cc2-6f742062f0a3",
|
||
"metadata": {
|
||
"id": "41fb78a4-5aa1-4288-9cc2-6f742062f0a3"
|
||
},
|
||
"source": [
|
||
"# Fine Tuning with Frontier Models"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "f8d0713f-0f79-460f-8acb-47afb877d24a",
|
||
"metadata": {
|
||
"id": "f8d0713f-0f79-460f-8acb-47afb877d24a",
|
||
"jp-MarkdownHeadingCollapsed": true
|
||
},
|
||
"source": [
|
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"## Utility"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2cdfe762-3200-4459-981e-0ded7c14b4de",
|
||
"metadata": {
|
||
"id": "2cdfe762-3200-4459-981e-0ded7c14b4de"
|
||
},
|
||
"outputs": [],
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||
"source": [
|
||
"# Constants - used for printing to stdout in color\n",
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"\n",
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"GREEN = \"\\033[92m\"\n",
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"YELLOW = \"\\033[93m\"\n",
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"RED = \"\\033[91m\"\n",
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"RESET = \"\\033[0m\"\n",
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"COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}"
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]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d9f325d5-fb67-475c-aca0-01c0f0ea5ec1",
|
||
"metadata": {
|
||
"id": "d9f325d5-fb67-475c-aca0-01c0f0ea5ec1",
|
||
"jp-MarkdownHeadingCollapsed": true
|
||
},
|
||
"source": [
|
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"### Item"
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||
]
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||
},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "0832e74b-2779-4822-8e6c-4361ec165c7f",
|
||
"metadata": {
|
||
"id": "0832e74b-2779-4822-8e6c-4361ec165c7f"
|
||
},
|
||
"outputs": [],
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||
"source": [
|
||
"from typing import Optional\n",
|
||
"from transformers import AutoTokenizer\n",
|
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"import re\n",
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"\n",
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"BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
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"\n",
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"MIN_TOKENS = 150 # Any less than this, and we don't have enough useful content\n",
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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\n",
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"\n",
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"MIN_CHARS = 300\n",
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"CEILING_CHARS = MAX_TOKENS * 7\n",
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"\n",
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"class Item:\n",
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" \"\"\"\n",
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" An Item is a cleaned, curated datapoint of a Product with a Price\n",
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" \"\"\"\n",
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"\n",
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" tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
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" PREFIX = \"Price is $\"\n",
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" QUESTION = \"How much does this cost to the nearest dollar?\"\n",
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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 \"]\n",
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"\n",
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" title: str\n",
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" price: float\n",
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" category: str\n",
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" token_count: int = 0\n",
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" details: Optional[str]\n",
|
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" prompt: Optional[str] = None\n",
|
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" include = False\n",
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"\n",
|
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" def __init__(self, data, price):\n",
|
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" self.title = data['title']\n",
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" self.price = price\n",
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" self.parse(data)\n",
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"\n",
|
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" def scrub_details(self):\n",
|
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" \"\"\"\n",
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" Clean up the details string by removing common text that doesn't add value\n",
|
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" \"\"\"\n",
|
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" details = self.details\n",
|
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" for remove in self.REMOVALS:\n",
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" details = details.replace(remove, \"\")\n",
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" return details\n",
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"\n",
|
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" def scrub(self, stuff):\n",
|
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" \"\"\"\n",
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" Clean up the provided text by removing unnecessary characters and whitespace\n",
|
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" Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers\n",
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" \"\"\"\n",
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" stuff = re.sub(r'[:\\[\\]\"{}【】\\s]+', ' ', stuff).strip()\n",
|
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" stuff = stuff.replace(\" ,\", \",\").replace(\",,,\",\",\").replace(\",,\",\",\")\n",
|
||
" words = stuff.split(' ')\n",
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" select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)]\n",
|
||
" return \" \".join(select)\n",
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"\n",
|
||
" def parse(self, data):\n",
|
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" \"\"\"\n",
|
||
" Parse this datapoint and if it fits within the allowed Token range,\n",
|
||
" then set include to True\n",
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" \"\"\"\n",
|
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" contents = '\\n'.join(data['description'])\n",
|
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" if contents:\n",
|
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" contents += '\\n'\n",
|
||
" features = '\\n'.join(data['features'])\n",
|
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" if features:\n",
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" contents += features + '\\n'\n",
|
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" self.details = data['details']\n",
|
||
" if self.details:\n",
|
||
" contents += self.scrub_details() + '\\n'\n",
|
||
" if len(contents) > MIN_CHARS:\n",
|
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" contents = contents[:CEILING_CHARS]\n",
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||
" text = f\"{self.scrub(self.title)}\\n{self.scrub(contents)}\"\n",
|
||
" tokens = self.tokenizer.encode(text, add_special_tokens=False)\n",
|
||
" if len(tokens) > MIN_TOKENS:\n",
|
||
" tokens = tokens[:MAX_TOKENS]\n",
|
||
" text = self.tokenizer.decode(tokens)\n",
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||
" self.make_prompt(text)\n",
|
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" self.include = True\n",
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"\n",
|
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" def make_prompt(self, text):\n",
|
||
" \"\"\"\n",
|
||
" Set the prompt instance variable to be a prompt appropriate for training\n",
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" \"\"\"\n",
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" self.prompt = f\"{self.QUESTION}\\n\\n{text}\\n\\n\"\n",
|
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" self.prompt += f\"{self.PREFIX}{str(round(self.price))}.00\"\n",
|
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" self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False))\n",
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"\n",
|
||
" def test_prompt(self):\n",
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" \"\"\"\n",
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" Return a prompt suitable for testing, with the actual price removed\n",
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" \"\"\"\n",
|
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" return self.prompt.split(self.PREFIX)[0] + self.PREFIX\n",
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"\n",
|
||
" def __repr__(self):\n",
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||
" \"\"\"\n",
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" Return a String version of this Item\n",
|
||
" \"\"\"\n",
|
||
" return f\"<{self.title} = ${self.price}>\"\n"
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||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "LaIwYGzItsEi",
|
||
"metadata": {
|
||
"id": "LaIwYGzItsEi"
|
||
},
|
||
"source": [
|
||
"### Tester"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "129470d7-a5b1-4851-8800-970cccc8bcf5",
|
||
"metadata": {
|
||
"id": "129470d7-a5b1-4851-8800-970cccc8bcf5"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"class Tester:\n",
|
||
"\n",
|
||
" def __init__(self, predictor, data, title=None, size=250):\n",
|
||
" self.predictor = predictor\n",
|
||
" self.data = data\n",
|
||
" self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
|
||
" self.size = size\n",
|
||
" self.guesses = []\n",
|
||
" self.truths = []\n",
|
||
" self.errors = []\n",
|
||
" self.sles = []\n",
|
||
" self.colors = []\n",
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||
"\n",
|
||
" def color_for(self, error, truth):\n",
|
||
" if error<40 or error/truth < 0.2:\n",
|
||
" return \"green\"\n",
|
||
" elif error<80 or error/truth < 0.4:\n",
|
||
" return \"orange\"\n",
|
||
" else:\n",
|
||
" return \"red\"\n",
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||
"\n",
|
||
" def run_datapoint(self, i):\n",
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" datapoint = self.data[i]\n",
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||
" guess = self.predictor(datapoint)\n",
|
||
" truth = datapoint.price\n",
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||
" error = abs(guess - truth)\n",
|
||
" log_error = math.log(truth+1) - math.log(guess+1)\n",
|
||
" sle = log_error ** 2\n",
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||
" color = self.color_for(error, truth)\n",
|
||
" title = datapoint.title if len(datapoint.title) <= 40 else datapoint.title[:40]+\"...\"\n",
|
||
" self.guesses.append(guess)\n",
|
||
" self.truths.append(truth)\n",
|
||
" self.errors.append(error)\n",
|
||
" self.sles.append(sle)\n",
|
||
" self.colors.append(color)\n",
|
||
" print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
|
||
"\n",
|
||
" def chart(self, title):\n",
|
||
" max_error = max(self.errors)\n",
|
||
" plt.figure(figsize=(12, 8))\n",
|
||
" max_val = max(max(self.truths), max(self.guesses))\n",
|
||
" plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
|
||
" plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
|
||
" plt.xlabel('Ground Truth')\n",
|
||
" plt.ylabel('Model Estimate')\n",
|
||
" plt.xlim(0, max_val)\n",
|
||
" plt.ylim(0, max_val)\n",
|
||
" plt.title(title)\n",
|
||
" plt.show()\n",
|
||
"\n",
|
||
" def report(self):\n",
|
||
" average_error = sum(self.errors) / self.size\n",
|
||
" rmsle = math.sqrt(sum(self.sles) / self.size)\n",
|
||
" hits = sum(1 for color in self.colors if color==\"green\")\n",
|
||
" title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
|
||
" self.chart(title)\n",
|
||
"\n",
|
||
" def run(self):\n",
|
||
" self.error = 0\n",
|
||
" for i in range(self.size):\n",
|
||
" self.run_datapoint(i)\n",
|
||
" self.report()\n",
|
||
"\n",
|
||
" @classmethod\n",
|
||
" def test(cls, function, data):\n",
|
||
" cls(function, data).run()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "6XywRUiUro69",
|
||
"metadata": {
|
||
"id": "6XywRUiUro69"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# A utility function to extract the price from a string\n",
|
||
"\n",
|
||
"def get_price(s):\n",
|
||
" s = s.replace('$','').replace(',','')\n",
|
||
" match = re.search(r'[-+]?\\d*\\.?\\d+', s) # Simplify regex\n",
|
||
" return float(match.group()) if match else 0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "10af1228-30b7-4dfc-a364-059ea099af81",
|
||
"metadata": {
|
||
"id": "10af1228-30b7-4dfc-a364-059ea099af81"
|
||
},
|
||
"source": [
|
||
"## Data Curation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "5faa087c-bdf7-42e5-9c32-c0b0a4d4160f",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "5faa087c-bdf7-42e5-9c32-c0b0a4d4160f",
|
||
"outputId": "b21530be-718f-4bed-aa23-16227f8a92c0"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%pip install --upgrade --quiet jupyterlab ipython ipywidgets huggingface_hub datasets transformers\n",
|
||
"\n",
|
||
"%matplotlib notebook\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3XTxVhq0xC8Z",
|
||
"metadata": {
|
||
"id": "3XTxVhq0xC8Z"
|
||
},
|
||
"source": [
|
||
"### Load from Hugging Face"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2bd6fc25-77c4-47a6-a2d2-ce80403f3c22",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 113,
|
||
"referenced_widgets": [
|
||
"cb25b81f26f14748a0496f1dcb8a4079",
|
||
"f902469b330c44ecb0b5a27892543a62",
|
||
"d4a96323719e4b19877dab1f20df76c2",
|
||
"77adf1d23ac84b0d9465afdd5d604f12",
|
||
"8549a500ad1e46cdbc8e839bec7fb2d2",
|
||
"0e5f4a86c9fb497198dba931ae3f5e34",
|
||
"6f4d626b3d414744a420da9e2af420f7",
|
||
"801e742fd5a94edbb698d80266ff0a12",
|
||
"675e6aa4d44148afb3f8f6c55e94807b",
|
||
"c29c01940e1040af8ac9d4c2d9a5d4e5",
|
||
"9a6cf9fb89184f3db6f4524533500025",
|
||
"7cba9a5cce3a4b899c8bfd0a3c6c8413",
|
||
"3e2642a0bb8a48bb8861252b8d221893",
|
||
"c0ec87cef19e49989d6379ea7e63e7fd",
|
||
"a781882db7ad49a0ae8cd4614754d2b6",
|
||
"c45b2735d3864dd9ab87628bea74f618",
|
||
"8dde01b656894092bb33d691e4dbe49b",
|
||
"4b5b7e98540840c69fdda8e3a9ce8947",
|
||
"5a642ca6bc8b4a52b83e9ce708f7561a",
|
||
"eff7608645cc450f9dc5a69bc96839c8",
|
||
"82ce2f089eba44e5ba17db5afc1729e7",
|
||
"7ec30523eb084f1abdd5002173780c15",
|
||
"796cb9a1e7154922a9235070b4eb0e83",
|
||
"58aed351dc524306909d796fb7a4b511",
|
||
"d55b5af25c5f4eb2bce121ea61811ebe",
|
||
"a30ce766b7444d9d9910a64538eee263",
|
||
"07d582edd37f41298d6be880b2c09fac",
|
||
"e5f3117210224c008ef84f8879437510",
|
||
"a525cd4b3b794a208d634891ffb1d334",
|
||
"ed265ba4024b45d9a3b6fd5cbb2a00c6",
|
||
"14dcae832c584937b0dc7bc7e17f7517",
|
||
"66ced28daca8492b8eeabfbcc62eec0f",
|
||
"7da27317676b462c9ba143f47b336cab"
|
||
]
|
||
},
|
||
"id": "2bd6fc25-77c4-47a6-a2d2-ce80403f3c22",
|
||
"outputId": "eed4636b-5c2a-4f7d-8283-af38b6baa213"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from datasets import load_dataset, Dataset, DatasetDict\n",
|
||
"from transformers import AutoTokenizer\n",
|
||
"\n",
|
||
"\n",
|
||
"dataset = load_dataset('ranskills/Amazon-Reviews-2023-raw_meta_All_Beauty', split='full')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b66b59c2-80b2-4d47-b739-c59423cf9d7d",
|
||
"metadata": {
|
||
"id": "b66b59c2-80b2-4d47-b739-c59423cf9d7d"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from IPython.display import display, JSON\n",
|
||
"\n",
|
||
"\n",
|
||
"print(f'Number of datapoints: {dataset.num_rows:,}')\n",
|
||
"display(JSON(dataset.features.to_dict()))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "e9620ed3-205e-48ee-b67a-e56b30bf6b6b",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 67,
|
||
"referenced_widgets": [
|
||
"fa83d76f75034101a2a531b3244ed61b",
|
||
"240f07ca195141bd973b34ac7ff5bc69",
|
||
"191aeaabe850445d925fc686a5030919",
|
||
"f30dd057a4394b1bb555427244efc8be",
|
||
"225e7ee50f624dc3bbaf682e1140e0d4",
|
||
"886cf1b0c3464afbbaf5c6a9a8d383f0",
|
||
"3c4f8e4a71b2405080eecc702fa51091",
|
||
"bb5cc6df3f1a4ab6add74bd40b654d2f",
|
||
"974e53ceb3bf4739bf8d5e497b9a58f1",
|
||
"1abdc3eabae647a784faf50ca04a4664",
|
||
"36f1ab21cb4842f5bb5ece32772ff57b"
|
||
]
|
||
},
|
||
"id": "e9620ed3-205e-48ee-b67a-e56b30bf6b6b",
|
||
"outputId": "c573a50b-b6b0-42a3-b3e6-97bff7a1c872"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def non_zero_price_filter(datapoint: dict):\n",
|
||
" try:\n",
|
||
" price = float(datapoint['price'])\n",
|
||
" return price > 0\n",
|
||
" except:\n",
|
||
" return False\n",
|
||
"\n",
|
||
"filtered_dataset = dataset.filter(non_zero_price_filter)\n",
|
||
"\n",
|
||
"print(f'Prices with non-zero prices:{filtered_dataset.num_rows:,} = {filtered_dataset.num_rows / dataset.num_rows * 100:,.2f}%')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "834a3c4b-fc9c-4bc7-b6b9-bdf7e8d6d585",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "834a3c4b-fc9c-4bc7-b6b9-bdf7e8d6d585",
|
||
"outputId": "5e2a46bb-3ca1-4727-9293-62877e0161b8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from collections import defaultdict\n",
|
||
"\n",
|
||
"import pandas as pd\n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"\n",
|
||
"\n",
|
||
"data = defaultdict(lambda: [])\n",
|
||
"for datapoint in filtered_dataset:\n",
|
||
" price = float(datapoint['price'])\n",
|
||
" contents = datapoint[\"title\"] + str(datapoint[\"description\"]) + str(datapoint[\"features\"]) + str(datapoint[\"details\"])\n",
|
||
"\n",
|
||
" data['price'].append(price)\n",
|
||
" data['characters'].append(len(contents))\n",
|
||
"\n",
|
||
"%matplotlib inline\n",
|
||
"\n",
|
||
"df = pd.DataFrame(data)\n",
|
||
"\n",
|
||
"combined_describe = pd.concat(\n",
|
||
" [df['price'].describe(), df['characters'].describe()],\n",
|
||
" axis=1\n",
|
||
")\n",
|
||
"\n",
|
||
"display(combined_describe)\n",
|
||
"\n",
|
||
"prices = data['price']\n",
|
||
"lengths = data['characters']\n",
|
||
"\n",
|
||
"plt.figure(figsize=(15, 6))\n",
|
||
"plt.title(f\"Prices: Avg {df['price'].mean():,.2f} and highest {df['price'].max():,}\\n\")\n",
|
||
"plt.xlabel('Length (chars)')\n",
|
||
"plt.ylabel('Count')\n",
|
||
"plt.hist(prices, rwidth=0.7, color=\"orange\", bins=range(0, 300, 10))\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"plt.figure(figsize=(15, 6))\n",
|
||
"plt.title(f\"Characters: Avg {sum(lengths)/len(lengths):,.0f} and highest {max(lengths):,}\\n\")\n",
|
||
"plt.xlabel('Length (characters)')\n",
|
||
"plt.ylabel('Count')\n",
|
||
"plt.hist(lengths, rwidth=0.7, color=\"lightblue\", bins=range(0, 2500, 50))\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a506f42c-81c0-4198-bc0b-1e0653620be8",
|
||
"metadata": {
|
||
"id": "a506f42c-81c0-4198-bc0b-1e0653620be8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"BASE_MODEL = 'meta-llama/Meta-Llama-3.1-8B'\n",
|
||
"tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n",
|
||
"\n",
|
||
"tokenizer.encode('114', add_special_tokens=False)\n",
|
||
"\n",
|
||
"items = []\n",
|
||
"for datapoint in filtered_dataset:\n",
|
||
" price = float(datapoint['price'])\n",
|
||
" items.append(Item(datapoint, price))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "5842ace6-332d-46da-a853-5ea5a2a1cf88",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "5842ace6-332d-46da-a853-5ea5a2a1cf88",
|
||
"outputId": "172d3a7e-5f0f-4424-ddcd-1ed909e9e02c"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"print(items[0].test_prompt())"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "42ee0099-0d2a-4331-a01c-3462363a6987",
|
||
"metadata": {
|
||
"id": "42ee0099-0d2a-4331-a01c-3462363a6987"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# filter out items with None prompt as a result of their content being below the minimum threshold\n",
|
||
"valid_items = [item for item in items if item.prompt is not None]\n",
|
||
"\n",
|
||
"data_size = len(valid_items)\n",
|
||
"\n",
|
||
"\n",
|
||
"training_size = int(data_size * 0.9)\n",
|
||
"train = valid_items[:training_size]\n",
|
||
"test = valid_items[training_size:]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "1146d5a2-f93e-4fe9-864e-4ce7e01e257b",
|
||
"metadata": {
|
||
"id": "1146d5a2-f93e-4fe9-864e-4ce7e01e257b"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"train_prompts = [item.prompt for item in train]\n",
|
||
"train_prices = [item.price for item in train]\n",
|
||
"test_prompts = [item.test_prompt() for item in test]\n",
|
||
"test_prices = [item.price for item in test]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "31ca360d-5fc6-487a-91c6-d61758b2ff16",
|
||
"metadata": {
|
||
"id": "31ca360d-5fc6-487a-91c6-d61758b2ff16"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Create a Dataset from the lists\n",
|
||
"\n",
|
||
"train_dataset = Dataset.from_dict({\"text\": train_prompts, \"price\": train_prices})\n",
|
||
"test_dataset = Dataset.from_dict({\"text\": test_prompts, \"price\": test_prices})\n",
|
||
"dataset = DatasetDict({\n",
|
||
" \"train\": train_dataset,\n",
|
||
" \"test\": test_dataset\n",
|
||
"})"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "05e6ca7e-bf40-49f9-bffb-a5b22e5800d8",
|
||
"metadata": {
|
||
"id": "05e6ca7e-bf40-49f9-bffb-a5b22e5800d8"
|
||
},
|
||
"source": [
|
||
"### Export Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b0ff2fe3-78bf-49e3-a682-6a46742d010c",
|
||
"metadata": {
|
||
"id": "b0ff2fe3-78bf-49e3-a682-6a46742d010c"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import pickle\n",
|
||
"\n",
|
||
"DATA_DIR = 'data'\n",
|
||
"\n",
|
||
"train_storage_file = lambda ext: f'{DATA_DIR}/all_beauty_train{ext}'\n",
|
||
"test_storage_file = lambda ext: f'{DATA_DIR}/all_beauty_test{ext}'\n",
|
||
"\n",
|
||
"with open(train_storage_file('.pkl'), 'wb') as file:\n",
|
||
" pickle.dump(train, file)\n",
|
||
"\n",
|
||
"with open(test_storage_file('.pkl'), 'wb') as file:\n",
|
||
" pickle.dump(test, file)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "b2164662-9bc9-4a66-9e4e-a8a955a45753",
|
||
"metadata": {
|
||
"id": "b2164662-9bc9-4a66-9e4e-a8a955a45753",
|
||
"outputId": "7bd7ff39-93d6-4886-f223-22fc36634828"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset['train'].to_parquet(train_storage_file('.parquet'))\n",
|
||
"dataset['test'].to_parquet(test_storage_file('.parquet'))\n",
|
||
"\n",
|
||
"# How to load back the data\n",
|
||
"# loaded_dataset = load_dataset(\"parquet\", data_files='amazon_polarity_train.parquet')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6fe428a2-41c4-4f7f-a43f-e8ba2f344013",
|
||
"metadata": {
|
||
"id": "6fe428a2-41c4-4f7f-a43f-e8ba2f344013"
|
||
},
|
||
"source": [
|
||
"### Predictions"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "qX0c_prppnyZ",
|
||
"metadata": {
|
||
"id": "qX0c_prppnyZ"
|
||
},
|
||
"source": [
|
||
"#### Random Pricer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "7323252b-db50-4b8a-a7fc-8504bb3d218b",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "7323252b-db50-4b8a-a7fc-8504bb3d218b",
|
||
"outputId": "5a2d6306-9709-4028-a83c-33170c629739"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import random\n",
|
||
"import math\n",
|
||
"\n",
|
||
"\n",
|
||
"def random_pricer(item):\n",
|
||
" return random.randrange(1,200)\n",
|
||
"\n",
|
||
"random.seed(42)\n",
|
||
"\n",
|
||
"# Run our TestRunner\n",
|
||
"Tester.test(random_pricer, test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "O0xVXRXkp9sQ",
|
||
"metadata": {
|
||
"id": "O0xVXRXkp9sQ"
|
||
},
|
||
"source": [
|
||
"#### Constant Pricer"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "6a932b0e-ba6e-45d2-8436-b740c3681272",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "6a932b0e-ba6e-45d2-8436-b740c3681272",
|
||
"outputId": "d6ee472e-7a10-4eac-ce5f-3ddd755f4f21"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"training_prices = [item.price for item in train]\n",
|
||
"training_average = sum(training_prices) / len(training_prices)\n",
|
||
"\n",
|
||
"def constant_pricer(item):\n",
|
||
" return training_average\n",
|
||
"\n",
|
||
"Tester.test(constant_pricer, test)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d3410bd4-98e4-42a6-a702-4423cfd034b4",
|
||
"metadata": {
|
||
"id": "d3410bd4-98e4-42a6-a702-4423cfd034b4",
|
||
"outputId": "2e57a75a-4873-4207-e28f-9d51a6359e56"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"train[0].details"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "44537051-7b4e-4b8c-95a7-a989ea51e517",
|
||
"metadata": {
|
||
"id": "44537051-7b4e-4b8c-95a7-a989ea51e517"
|
||
},
|
||
"source": [
|
||
"### Prepare Fine-Tuning Data"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "47d03b0b-4a93-4f9d-80ac-10f3fc11ccec",
|
||
"metadata": {
|
||
"id": "47d03b0b-4a93-4f9d-80ac-10f3fc11ccec"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"fine_tune_train = train[:100]\n",
|
||
"fine_tune_validation = train[100:125]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4d7b6f35-890c-4227-8990-6b62694a332d",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "4d7b6f35-890c-4227-8990-6b62694a332d",
|
||
"outputId": "43deeec1-bde8-4651-9ce8-6c4b4aa39f8a"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def messages_for(item):\n",
|
||
" system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n",
|
||
" user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n",
|
||
" return [\n",
|
||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||
" {\"role\": \"user\", \"content\": user_prompt},\n",
|
||
" {\"role\": \"assistant\", \"content\": f\"Price is ${item.price:.2f}\"}\n",
|
||
" ]\n",
|
||
"\n",
|
||
"messages_for(train[0])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "1a6e06f3-614f-4687-bd43-9ac03aaface8",
|
||
"metadata": {
|
||
"id": "1a6e06f3-614f-4687-bd43-9ac03aaface8"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import json\n",
|
||
"from pathlib import Path\n",
|
||
"DATA_DIR = 'data'\n",
|
||
"\n",
|
||
"data_path = Path(DATA_DIR)\n",
|
||
"\n",
|
||
"def make_jsonl(items):\n",
|
||
" result = \"\"\n",
|
||
" for item in items:\n",
|
||
" messages = messages_for(item)\n",
|
||
" messages_str = json.dumps(messages)\n",
|
||
" result += '{\"messages\": ' + messages_str +'}\\n'\n",
|
||
" return result.strip()\n",
|
||
"\n",
|
||
"# print(make_jsonl(train[:3]))\n",
|
||
"data_path.absolute()\n",
|
||
"if not data_path.exists():\n",
|
||
" data_path.mkdir(parents=True)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"train_jsonl_path = f'{data_path}/pricer_train.jsonl'\n",
|
||
"validation_jsonl_path = f'{data_path}/pricer_validation.jsonl'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d8dda552-8003-4fdc-b36a-7d0afa9b0b42",
|
||
"metadata": {
|
||
"id": "d8dda552-8003-4fdc-b36a-7d0afa9b0b42"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def write_jsonl(items, filename):\n",
|
||
" with open(filename, \"w\") as f:\n",
|
||
" jsonl = make_jsonl(items)\n",
|
||
" f.write(jsonl)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "189e959c-d70c-4509-bff6-1cbd8e8db637",
|
||
"metadata": {
|
||
"id": "189e959c-d70c-4509-bff6-1cbd8e8db637"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"\n",
|
||
"write_jsonl(fine_tune_train, train_jsonl_path)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "6b1480e2-ed19-4d0e-bc5d-a00086d104a2",
|
||
"metadata": {
|
||
"id": "6b1480e2-ed19-4d0e-bc5d-a00086d104a2"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"write_jsonl(fine_tune_validation, validation_jsonl_path)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ga-f4JK7sPU2",
|
||
"metadata": {
|
||
"id": "ga-f4JK7sPU2"
|
||
},
|
||
"source": [
|
||
"## Training"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "de958a51-69ba-420c-84b7-d32765898fd2",
|
||
"metadata": {
|
||
"id": "de958a51-69ba-420c-84b7-d32765898fd2"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import os\n",
|
||
"from openai import OpenAI\n",
|
||
"from dotenv import load_dotenv\n",
|
||
"from google.colab import userdata\n",
|
||
"\n",
|
||
"load_dotenv()\n",
|
||
"os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY')\n",
|
||
"\n",
|
||
"openai = OpenAI()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "QFDAoNnoRCk1",
|
||
"metadata": {
|
||
"id": "QFDAoNnoRCk1"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"with open(train_jsonl_path, 'rb') as f:\n",
|
||
" train_file = openai.files.create(file=f, purpose='fine-tune')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "kBVWisusQwDq",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "kBVWisusQwDq",
|
||
"outputId": "79dbe38a-cb76-4b8d-bd13-95b2f5ed8270"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"train_file"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"id": "wgth1KvMSEOb",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "wgth1KvMSEOb",
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"outputId": "9191cae8-ae86-4db1-b7cb-02c53add139c"
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},
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"outputs": [],
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"source": [
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"with open(validation_jsonl_path, 'rb') as f:\n",
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" validation_file = openai.files.create(file=f, purpose='fine-tune')\n",
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"\n",
|
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"validation_file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "-ohEia37Sjtx",
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"metadata": {
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"id": "-ohEia37Sjtx"
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||
},
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"outputs": [],
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"source": [
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||
"wandb_integration = {\"type\": \"wandb\", \"wandb\": {\"project\": \"gpt-pricer\"}}"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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"execution_count": null,
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"id": "g7uz8SC5S3_s",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "g7uz8SC5S3_s",
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"outputId": "751e5087-8dcb-4dcb-f9eb-5b95f28c828c"
|
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},
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"outputs": [],
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"source": [
|
||
"openai.fine_tuning.jobs.create(\n",
|
||
" training_file=train_file.id,\n",
|
||
" validation_file=validation_file.id,\n",
|
||
" model=\"gpt-4o-mini-2024-07-18\",\n",
|
||
" seed=42,\n",
|
||
" hyperparameters={\"n_epochs\": 1},\n",
|
||
" integrations = [wandb_integration],\n",
|
||
" suffix=\"pricer\"\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
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||
"id": "_zHswJwzWCHZ",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
|
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},
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"id": "_zHswJwzWCHZ",
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"outputId": "a6899370-4332-4445-d2fd-b0e9a3140d78"
|
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},
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"outputs": [],
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"source": [
|
||
"openai.fine_tuning.jobs.list(limit=1)"
|
||
]
|
||
},
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{
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"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "rSHYkQojWH8Q",
|
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"metadata": {
|
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"colab": {
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"base_uri": "https://localhost:8080/",
|
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"height": 35
|
||
},
|
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"id": "rSHYkQojWH8Q",
|
||
"outputId": "a52772de-12bb-460c-bed7-2ef2d21892ee"
|
||
},
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"outputs": [],
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"source": [
|
||
"job_id = openai.fine_tuning.jobs.list(limit=1).data[0].id\n",
|
||
"job_id"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "Yqq-jd1yWMuO",
|
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
||
},
|
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"id": "Yqq-jd1yWMuO",
|
||
"outputId": "0a758e9e-d7e5-4c44-fcef-5969352729f9"
|
||
},
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"outputs": [],
|
||
"source": [
|
||
"openai.fine_tuning.jobs.retrieve(job_id)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "37BH0u-QWOiY",
|
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"metadata": {
|
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"colab": {
|
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"base_uri": "https://localhost:8080/"
|
||
},
|
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"id": "37BH0u-QWOiY",
|
||
"outputId": "7d8b3ff3-82db-47c9-80c3-b3c1de60e469"
|
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},
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"outputs": [],
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"source": [
|
||
"openai.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10).data"
|
||
]
|
||
},
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{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2nNSE_AzWYMq",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 955
|
||
},
|
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"id": "2nNSE_AzWYMq",
|
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"outputId": "ac6904ff-28f8-4c72-a064-8c1b72437e49"
|
||
},
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"outputs": [],
|
||
"source": [
|
||
"import wandb\n",
|
||
"from wandb.integration.openai.fine_tuning import WandbLogger\n",
|
||
"\n",
|
||
"# Log in to Weights & Biases.\n",
|
||
"wandb.login()\n",
|
||
"# Sync the fine-tuning job with Weights & Biases.\n",
|
||
"WandbLogger.sync(fine_tune_job_id=job_id, project=\"gpt-pricer\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "ASiJUw-Fh8Ul",
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 35
|
||
},
|
||
"id": "ASiJUw-Fh8Ul",
|
||
"outputId": "037b1430-cffe-4c4d-b0a6-38262f1fecd4"
|
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},
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"outputs": [],
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"source": [
|
||
"fine_tuned_model_name = openai.fine_tuning.jobs.retrieve(job_id).fine_tuned_model\n",
|
||
"fine_tuned_model_name"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "7jB_7gqBiH_r",
|
||
"metadata": {
|
||
"id": "7jB_7gqBiH_r"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def messages_for(item):\n",
|
||
" system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n",
|
||
" user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n",
|
||
" return [\n",
|
||
" {\"role\": \"system\", \"content\": system_message},\n",
|
||
" {\"role\": \"user\", \"content\": user_prompt},\n",
|
||
" {\"role\": \"assistant\", \"content\": \"Price is $\"}\n",
|
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" ]"
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]
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||
},
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{
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"cell_type": "code",
|
||
"execution_count": null,
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"id": "BHfLSadhiVQE",
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"metadata": {
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"id": "BHfLSadhiVQE"
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},
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"outputs": [],
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"source": [
|
||
"# The function for gpt-4o-mini\n",
|
||
"\n",
|
||
"def gpt_fine_tuned(item):\n",
|
||
" response = openai.chat.completions.create(\n",
|
||
" model=fine_tuned_model_name,\n",
|
||
" messages=messages_for(item),\n",
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" seed=42,\n",
|
||
" max_tokens=7\n",
|
||
" )\n",
|
||
" reply = response.choices[0].message.content\n",
|
||
" return get_price(reply)"
|
||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "C0CiTZ4jkjrI",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "C0CiTZ4jkjrI",
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"outputId": "640299e8-ebeb-4562-bcd5-d3bab726e557"
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||
},
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"outputs": [],
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"source": [
|
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"print(test[0].price)\n",
|
||
"print(gpt_fine_tuned(test[0]))"
|
||
]
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||
},
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{
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"cell_type": "code",
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},
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"outputs": [],
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"Tester.test(gpt_fine_tuned, test)"
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]
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