{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "GHsssBgWM_l0" }, "source": [ "\n", "\n", "## Predict Product Prices\n", "\n", "### Evaluate our fine-tuned open source model\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MDyR63OTNUJ6", "outputId": "e7955fe4-b736-4c77-a0cb-31d9a4c8031b" }, "outputs": [], "source": [ "# pip installs\n", "\n", "!pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n", "!pip install -q --upgrade requests==2.32.3 bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 datasets==3.2.0 peft==0.14.0 trl==0.14.0 matplotlib wandb" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-yikV8pRBer9" }, "outputs": [], "source": [ "# imports\n", "\n", "import re\n", "import math\n", "from tqdm import tqdm\n", "from google.colab import userdata\n", "from huggingface_hub import login\n", "import torch\n", "import torch.nn.functional as F\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n", "from datasets import load_dataset, Dataset, DatasetDict\n", "from datetime import datetime\n", "from peft import PeftModel\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uuTX-xonNeOK" }, "outputs": [], "source": [ "# Constants\n", "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n", "PROJECT_NAME = \"pricer\"\n", "HF_USER = \"muthash\"\n", "\n", "# The run itself\n", "RUN_NAME = \"2025-10-28_20.07.34\"\n", "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n", "REVISION = None\n", "FINETUNED_MODEL = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n", "\n", "# Data\n", "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n", "\n", "# Hyperparameters for QLoRA\n", "QUANT_4_BIT = True\n", "\n", "%matplotlib inline\n", "\n", "# Used for writing to output in color\n", "\n", "GREEN = \"\\033[92m\"\n", "YELLOW = \"\\033[93m\"\n", "RED = \"\\033[91m\"\n", "RESET = \"\\033[0m\"\n", "COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "WyFPZeMcM88v" }, "outputs": [], "source": [ "# Log in to HuggingFace\n", "\n", "hf_token = userdata.get('HF_TOKEN')\n", "login(hf_token, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "3110b2c3fcea42dda6327d9987afeeb2", "57cee483f1fa409085b1e9e43a9b4f8d", "7be485f5f4bc4af38a9ca9c134840732", "7ae66dd70ae740499e843e02b07d5a9b", "14cf43474edb46799f13a7d1cc6fdf2b", "4d0ac7e0f72b46ad9ff56f97fbe5e600", "b916ea8c57ad437ca8c14102db6ca619", "4d4a15e9897643e6958e85ce15357040", "54539f1a030f4968b81ee26a661bdcd5", "d6f1d733b5434df2ba92d8720f7e2396", "e8362349f12e43a99ac4a2a2450eb7f5", "535bb6f2fa5d48d1bebfbe566273c0f3", "71d757026b494dd6bd83f330f727bc8a", "e94a96ce35e647d7b323c361c9eb66b9", "86297d422ad64c1a9f5973a68afb17f5", "f9676625923a4564bc7fe3ca672c7e62", "e7cf633b84e041a2b88d61e640926454", "e297c2a5a33d4f1084c5aee0ab723cd7", "3d2882d5e6e94995bf746e893b7dffe1", "5a727d27b8d44556907265d00468aaa7", "e0902ec2954e44e187496a0d37c5cb4a", "661a4b8a8cf649eabaa928e7c1fee875", "1f0b6c43692d4aa48ee0e58af7de15a2", "b4cf71e6820142278087b8d9f26e534f", "fffc75fe10084376851fdabc9fe2f362", "2d2e402e13b44cbeaad849facd8c71f1", "3dacd71a69be489fa64df9df9a938300", "b8660580b49c4ef3a7de03911a282d8d", "0d89c9d2a99145308cfc42fd804401b1", "7897a3fc1ec0490a8acd15264dc1f3a8", "c590b51dea0948b3a2f2fd624337a072", "67b199037e404a0cb55b94e899246371", "743cf6ec4d844980aa6030f34ae73db9", "bcb1b76b40a44b9b916a99e55422e542", "8e874eb56a6740c5aa7a13d0757c5ca9", "a36ccdce1402422db762987a8cec59f9", "947d72d8b2a0447b9632093e86404228", "fdd708defc1d4100b57ad6674a41455e", "13f8956182e443adae68f433eaf5831c", "5a0e69ef5e0d40fea8d11b299d89a04d", "0a107cdcc4d2450dbb4ea2e203f207e3", "422451340a964826bd1a4f1a1a49759b", "77fbdc1107794806911e4d38e7fbd06a", "3f3105e5d36f44f6ad763b491b4d348e", "390d46404d4849509bb36368f89d1843", "2d8c66ba83ca4f9aaf7380e3390e73ed", "d7cf3394663d49759291d97e63cf1457", "ebc20a78000a4f749b418fa36b0a3241", "e88506e862a449cdabde8607aa723f88", "d6f5220aad7f4e57b4d1030c004b8ee0", "6f947fc1d4224856ab4d813079078a43", "90dff77c89924eb59ebd446fdd3e8394", "142825a8ebdc4a2696759bba3ab04180", "f50835905342472aaf02200586e4066b", "a41a8a1080b7481fbb1c74e96c5a3d06" ] }, "id": "cvXVoJH8LS6u", "outputId": "c30211cb-f7fe-4c00-ea51-48707f1daf0f" }, "outputs": [], "source": [ "dataset = load_dataset(DATASET_NAME)\n", "train = dataset['train']\n", "test = dataset['test']\n", "test[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lAUAAcEC6ido" }, "outputs": [], "source": [ "# quantization\n", "\n", "if QUANT_4_BIT:\n", " quant_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_use_double_quant=True,\n", " bnb_4bit_compute_dtype=torch.bfloat16,\n", " bnb_4bit_quant_type=\"nf4\"\n", " )\n", "else:\n", " quant_config = BitsAndBytesConfig(\n", " load_in_8bit=True,\n", " bnb_8bit_compute_dtype=torch.bfloat16\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 482, "referenced_widgets": [ "daf4cb8bfe724eefa95c9449eb71ec22", "dd0a50e450eb49b7b81ef1cd73e6ff55", "cbd69a8336bd4a969c2e31c93ec9f43d", "87aaf9baa56948ca8b208a7549059ccb", "d227762244a84bb9bac57aad35cc708b", "77b42f462b954025b90130c79b364b85", "208613f037af4e62a7621aee883c6c6e", "c5151210030342d6a30d29819eff4e59", "da56cd57f6024456b36f12e8da786c6a", "298f56996bf94f94a0683497cd7c31af", "1388c259b0514b7b853bb77232ca1cf3", "49641387c5cd46d4bf04ab8f86f59c74", "4e2c838c23ad440da09761fe8554350c", "9e1b3d9bcb214bc9bd77c4c6ac27722a", "078e2a40010d43339927d07d120593ca", "dc76518388e846e6b942654df9a5be76", "e1b26d28b72944a9845002394f0181c8", "cbf6dc84b7b24ae8b7b612694eafcec7", "6c27c8c8d5c14cc3823224d73457165f", "1cd8c749ce6a4afaa8ef5ec0be4c4030", "fcb8699fd54e4c6bb54a0b0a277e3653", "1888b8a2f6b644f4a2b4de0aed48e834", "3fd1d7c0d9054c8f92742023f2e5a9b6", "91fc1e67228747ac8c09653cfc72e4fb", "4e6fbff7b7ba46958260f6ff5fb50094", "d53fca806c4a44dda9ad7f2a25e417d8", "b79db4fa9e9341728ce7c803fec2e175", "62bb2e9b15a845f79a89aa5e2e1d65af", "a0c64c19620e45a8ba31c020356ba422", "b7712b8f1438452cb553aec21ccab423", "f113adab1e1a46048658aa04fb1b78bb", "f707abd0d3f74e9a8f8e281491cd2258", "25035aca302a42889c2889915476cdc4", "1ca4c162e193457d9a22dca6a5c7e15f", "2e8cbdcd02404e2faa418d9d85f2311b", "7e4be34566cf465f88b62870478a5b5f", "ef20b18b55e94ee785aab0be6fc81dee", "f34291d28aa046618e97af2045bd15be", "d39d090e6dab475191b2c684ed422318", "c8be38fc18ef4f70800c0c48419a1f47", "18f602c3c1c34141b2f57dbe9a9674be", "1f5372abeef645728bb4f2d6819b6685", "6777ad52f78d49a3865b511e62e192b5", "43532489847c4f50b245eb5e34f6c625", "e166a07d741a4f3895975d87b7dde283", "ce3e385b37bb439bbafb5bd79c9566a5", "a5732be513ad4325948e859cc9a38085", "5780bea5bd424cd09ebfb1e548a3220d", "031b770be6ae4154adac7df5e57495c5", "61bceac429044096924c3224e487e7b1", "6ab602b8f4094c039fb3baa880954a60", "740662319af04f59b9ccc64ddf3b42a4", "e67ea654682646b68b669252da29834f", "6fbe710633f34f1b98f1eccce5cf1b82", "b984e2b2e27e42df8ae62714003809c7", "e1e67ac5ccc64304b8bffc6cf81c49b8", "1fd33a4017bf4576b118358edf564039", "fb6dc9400f394379acbfc598a23a39f6", "7facb2f3175548ea81e881cdb8b5a27c", "13a044064c094b2699e8815f49a7a89b", "e98365f5b6074362951886d2322d41fc", "faddc5dc1a21465190750b74e7ddd4de", "43f8848331b74439b23075c7af567e18", "f5f36c806afb4e4aa6c36d8498f315d0", "280016d5eb9c476ca6177f1c6ad2dd1f", "5bbb7bb563c7424a9d1e94b1ca7d66a9", "44ffd9301662474bb2408c128c5f15fc", "aace559622f14f3db64eaca683e40825", "1af4cdbee3414b5eae4b9423bfdeb5f1", "7939a14107ed4e71ac713f2e3ef95f82", "ffa1df7c26df44e0a895602987ae72a0", "8fc65d33b32344d5a6c839eb5be9205c", "5f87b24b2d8944e7a36e1c7216c1e6aa", "37719fed32d848958dad6b65bbd37f36", "650424b77fb8408184960632e09e5abc", "8d0647c5c6854ddb940da2cddb81eb9e", "72d61e6aaed848e4b0699e6e06f348c7", "e684cda5322547a08540c0a133d5b477", "8b807eccadfb4ca180a2183bbe6edb74", "b00a221e47694c7caf09d492157f0c0d", "3873705babbd4c3ebb7cabb657211878", "e903149354bc473eae6437d3d0f94415", "39b6e2f8da2f449d8ae3b982e607bc85", "d95f4b1c42d745a29fad309cbd13ca4d", "5e99a3087ad0439f910ca675d1e4824d", "77fe8a5bc9ee4e3fb13cef814629e1ae", "aae3b7be7f7d439d956de35c809d0497", "4a117632140a44718a6cecd8ca81d097", "0021b73b16a34853979a94478daf66dd", "a62b5509355b425796dc819f262ea179", "64a9ac361b9b4ba1a90f2719745f1348", "35cdf841c0634ee5bc54068c23e3a40a", "e08deb4be74344b9858bb6365676de5a", "624cfaa9045a404fa100dd4991450f58", "480603793e4c42c680e83ac77be4e5ca", "f6b48b35c25f4ab3bca16bad060bc852", "05f8cbd0b54346ccb3ac0993aeecc01a", "b20631493afa40a9805b3c84c6e6622f", "bc79bd0bf7bc4cdc962ac50ca64cb346", "802441f48e6b4aada289936a80d6b881", "837470db003644f29b6572d9a8461183", "a1b9a687e5304ac7a27ccca5169dbf1c", "dc13b5573e99472f9a6a96cd6794ac2c", "a4e2cf5da1214297af9f82e39d4ae26e", "fcda103d665b4b35b73d7fe99df3ae44", "283d3740c6a54d92bad153c724e06da8", "52cfa5953b4f408d97e58aef092b95ed", "e319f12fd0e747939d4aed4216923f81", "9a7eb7639e9b48eeac59441c7e7afc31", "9930b34d6d8f420d85f1c202b82c070a", "1c7eb88bad654fd9b295a3503ae2f9c0", "1c3cd585dc1c4a40a988cb89ef8c9e92", "1ac8bad0cbc34e6db78194929b2e013a", "98cc639e079145549e6692403f52f819", "ff267677259a4b5489dfedde3293defe", "3ce14b2e9d9341ccad8dad4b2ad03dca", "b918753beee84461adade5398f85c445", "82d606261fa44278870a692ed8d29fb3", "c2bbaa4f7c024eaba4d62199c97b841b", "067940bccd904c3a8165dc6eeb52f66b", "74dd38eae38144a48690d843779f4373", "a65a257de6904e1ca85e4252bfb37f16", "a36a9970050c40298a686df49369fbdf", "1b16b69250c24848af518dde63196ca1", "b29f1bbb50e348d586c1507cac895e1e", "acf3631df64c41799c90731c8056d38b", "ab50bbff41af4fdf9ace6429386cf003", "60c0b4c9b5314273b6901b230f71c6bd", "dab717cee5344e879b0c1049c167eb9d", "a33d99f710784b12bcc72910b46bf295", "544ebebee574472da8943adf25193f65", "b11b2fdf347f47d6979ed23e33010021", "91f3a225cbd94385aee3b4e2c34e4c93", "b9487ea6197f4721888db3a6ec4f1b00", "a6344883db2f4688accd01a7cfb5129e", "10b974892971426784bfa9a9afa473dc", "458771eefdb243769343e3bad663dfc9", "81cc49e927cf47ae8cea8b80b4a57acd", "f7b6c6d247fe4b38b2b4791e163e2c78", "7e20a3737fc94fec97cd4c64ed2c3158", "909252a5f4b04a6d9eb68d05897082f8", "4017fdd98d5e46aea0c274874a67ca05", "cac2a8f93e864f398819ed3e220903a0", "0717d812c1cf4d9d801a276d8187e3fd", "10db2e57dfd4468ab91fd8112c2521aa", "b9588c7ed7c74662ba4cc229b4f29e48", "c01351324dc74fb6ac2b3a260b61a147", "67fefc7d48cd4371865d28f81e7a79a6", "1b3017363911455f94943ff37a9bdf00", "51ab085101a44d15a7f4ce4ece08f934", "79a977df2774484ba38064c94765603d", "de8e9ba702d74e0eab82d73b18ea1965", "38955aed4f6f464abe7e2ff4d7555e0e", "d44df58b04a846948f5d13397294e60c" ] }, "id": "R_O04fKxMMT-", "outputId": "7e696baa-8fed-4281-d67f-1f55ea6a38e0" }, "outputs": [], "source": [ "# Load the Tokenizer and the Model\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n", "tokenizer.pad_token = tokenizer.eos_token\n", "tokenizer.padding_side = \"right\"\n", "\n", "base_model = AutoModelForCausalLM.from_pretrained(\n", " BASE_MODEL,\n", " quantization_config=quant_config,\n", " device_map=\"auto\",\n", ")\n", "base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n", "\n", "# Load the fine-tuned model with PEFT\n", "if REVISION:\n", " fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)\n", "else:\n", " fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL)\n", "\n", "\n", "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Qst1LhBVAB04" }, "outputs": [], "source": [ "def extract_price(s):\n", " if \"Price is $\" in s:\n", " contents = s.split(\"Price is $\")[1]\n", " contents = contents.replace(',','')\n", " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", contents)\n", " return float(match.group()) if match else 0\n", " return 0\n", "\n", "extract_price(\"Price is $a fabulous 899.99 or so\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Je5dR8QEAI1d" }, "outputs": [], "source": [ "top_K = 3\n", "\n", "def improved_model_predict(prompt, device=\"cuda\"):\n", " set_seed(42)\n", " inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n", " attention_mask = torch.ones(inputs.shape, device=device)\n", "\n", " with torch.no_grad():\n", " outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n", " next_token_logits = outputs.logits[:, -1, :].to('cpu')\n", "\n", " next_token_probs = F.softmax(next_token_logits, dim=-1)\n", " top_prob, top_token_id = next_token_probs.topk(top_K)\n", " prices, weights = [], []\n", " for i in range(top_K):\n", " predicted_token = tokenizer.decode(top_token_id[0][i])\n", " probability = top_prob[0][i]\n", " try:\n", " result = float(predicted_token)\n", " except ValueError as e:\n", " result = 0.0\n", " if result > 0:\n", " prices.append(result)\n", " weights.append(probability)\n", " if not prices:\n", " return 0.0, 0.0\n", " total = sum(weights)\n", " weighted_prices = [price * weight / total for price, weight in zip(prices, weights)]\n", " return sum(weighted_prices).item()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "30lzJXBH7BcK" }, "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", "\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", "\n", " def run_datapoint(self, i):\n", " datapoint = self.data[i]\n", " guess = self.predictor(datapoint[\"text\"])\n", " truth = datapoint[\"price\"]\n", " error = abs(guess - truth)\n", " log_error = math.log(truth+1) - math.log(guess+1)\n", " sle = log_error ** 2\n", " color = self.color_for(error, truth)\n", " title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\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, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "W_KcLvyt6kbb", "outputId": "29a71021-5209-4da8-dc60-6129059b6edf" }, "outputs": [], "source": [ "Tester.test(improved_model_predict, test)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0021b73b16a34853979a94478daf66dd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": 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