{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "GHsssBgWM_l0" }, "source": [ "\n", "\n", "## Predict Product Prices\n", "\n", "### And now, to 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": "eba91112-144e-41b2-91c4-e6b2ad338948" }, "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 os\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", "import transformers\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", "\n", "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n", "PROJECT_NAME = \"pricer\"\n", "HF_USER = \"ampelox\" # your HF name here! Or use mine if you just want to reproduce my results.\n", "\n", "# The run itself\n", "\n", "RUN_NAME = \"2025-10-30_09.40.59\"\n", "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n", "REVISION = \"dd79bbfe3922ac56eeba2b2473ca35b08beedaa4\" # or REVISION = None\n", "FINETUNED_MODEL = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n", "\n", "# Uncomment this line if you wish to use my model\n", "# FINETUNED_MODEL = f\"ed-donner/{PROJECT_RUN_NAME}\"\n", "\n", "# Data\n", "\n", "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n", "# Or just use the one I've uploaded\n", "# DATASET_NAME = \"ed-donner/pricer-data\"\n", "\n", "# Hyperparameters for QLoRA\n", "\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": "markdown", "metadata": { "id": "8JArT3QAQAjx" }, "source": [ "### Log in to HuggingFace\n", "\n", "If you don't already have a HuggingFace account, visit https://huggingface.co to sign up and create a token.\n", "\n", "Then select the Secrets for this Notebook by clicking on the key icon in the left, and add a new secret called `HF_TOKEN` with the value as your token.\n" ] }, { "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": 233, "referenced_widgets": [ "ffd4d0873de54f8facd041dcbf481105", "4db0c65fa1ea4586b0d3c600513a6e53", "0da2106789254bd5bc16b1fdbf86c118", "20b77b3d83aa49c3b2b5fda58e5efc59", "5c064fbdd5ba40d8a15bbcc1a97434b0", "1b0c4d269617449f93fda490ca5c70e5", "1a7c0f77fbbd40ea8ceb2674f4a6fb1e", "1f23ebbce9e24f0981b5f7d4f8f9b507", "bf7012b2760545faa96a239a12b4eddb", "8298e5ae7abb480d8d136d56e443c515", "9a9fb639510741c19f4d14be6994d92a", "3f71cbd40e334f6da5661f8daa02945f", "82a5916a81084df2a4616037ccdef025", "7c3639f3d4854ae2978821f1c8bc0670", "6b7fab4627af4a93baddac4210e2a9ea", "7cae9ff5d132426cadc874cfbaffba06", "2272983d621443b0ad47863156c0e0c3", "8bffcf4ebe9642c68a707d9d992a4fee", "eda741669314433287edb5b1e87ba81a", "489f90672d064cb1bef28fa02ca48677", "72009c83a3ff4a26899631fcc98a60e4", "49fe2b8355ca4d11974129913d0b7ba9", "ca5e0eccdb524b31a7da4b2f733b799b", "e5e9406ac05e48589c997c63198dd0df", "1eb8703f483649729ecb875955a9ff1a", "455a0653a65e415eb381a113e5a99ab1", "4f53585125bd49be9987d4501cac03d7", "67ad8b5959b44f83aa66460d7cea6e39", "87b6fa11864943e4a15db692c48ad449", "6aa4155e792344a0bff9d9e5f9407515", "421b53e43e404425a659033fa4e8c71f", "2e210420e0c9468886b90540646d5abb", "636c9c4257b64e6babbf26ba90b42d0b", "9506aa9ba454461cbed217b005cea710", "d46df04021684c2fb44fc9211b4be791", "de34dfd1f0ce446fb41751092b3f663d", "123fab0dbc084b54b30dd46902a960d7", "d3eaf4e902444290be911d8232c350c4", "25bbaf4c01ab468685ffedbba320a78b", "0be6499e4c8c4772b75aec98c021ce04", "a7641f0b3b3f4193bf789f09d1452c42", "d206729f54954c54bb0ef6c375e4c463", "c23239b8b047463ab2b9461aada0f1e9", "763e5d6d49c14c9b8187518264fac08d", "3d35322adec449949405136a45acb160", "c2648e7640974418a991b7c50e0c89f6", "eeac64b7636141f8ada469897a2aabcc", "f23fbf922fb84fedb21adf6553fce54c", "4b6a5400d0964092afe03300f8443531", "673538ecc8b846ee9aaa8d72ef426082", "917066ea6a53484ba3895e35a7a6cd4f", "7035b98fdadf40b684023d07eb59f4b7", "7f2b5cf2c72849acaaca98fe537c217b", "98b67ec0e2d64a68bf9459ba8318ea5a", "b5eb1f1846f34f70a18d325315b0ea77" ] }, "id": "cvXVoJH8LS6u", "outputId": "6ff12ecc-fa8b-4f7f-afc9-3690f52bc6d6" }, "outputs": [], "source": [ "dataset = load_dataset(DATASET_NAME)\n", "train = dataset['train']\n", "test = dataset['test']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xb86e__Wc7j_", "outputId": "d398aca0-3e80-46e7-e9e3-fd4a7ca0408d" }, "outputs": [], "source": [ "test[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "qJWQ0a3wZ0Bw" }, "source": [ "## Now load the Tokenizer and Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "lAUAAcEC6ido" }, "outputs": [], "source": [ "# pick the right quantization (thank you Robert M. for spotting the bug with the 8 bit version!)\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": 594, "referenced_widgets": [ "9d3b4350362044e18f0cfb80763f0cfb", "40203153a8cd4e919398458c75ff3b3b", "c96b4c657fc3494aa44d390e6b7cf341", "27fac4bc47664f29864614869cfbba23", "1bf3c6e347524a028280e00496d89b13", "e0bf0c14330c495ca2225c94e0579959", "52fb2ed81bac45dc91a7b3fa601b184e", "9e96a0821aba41969136a1f8b3ad0414", "31c0ae2670eb4f8d9f9c76b79cdeedd4", "f83b54262691436283c4f51a559e8011", "0833ef4e585546b7b6597fd2bbc75513", "afb4793df43442bbb19ed1a4e867714d", "e2c782671e614d21895f6b6971f9a9b2", "e280ff82acd64ff6b8587b299be5cc1b", "2dc0bb364df54911a89df8cc0d90a13c", "72e33e0b2d2947ca9be4e6807219d2ca", "b9d6e9adf15a4571a8390b2531984285", "84742d657466472da5fe8dbc26c983e1", "87e52c0c7f0b459386f2e1912c6f350c", "576b0fe56090423fb587e310f5c5a3ba", "c0615d02f1144ddca769e95020100d29", "99aa8378d7514ea8bbabc653ef69d063", "953f966958c944318313c1e0dcbe40c2", "10e96ef33cfe4c2eabbb7fc701fc871a", "b4c18d9482654ca58c27ce4f13f76a2a", "ba6d0785fec04368a5564bb677a4cceb", "1017a319c45e4de9a616ea96530e68da", "8bdb6c862bec486fb7b0c862cdcba5b7", "23d5829c4db743cbbfaa83a38ae0dfe6", "7c3a4188af4749bc8f96f2481f247f13", "8cb417fe916c465aa5c43683a7c3e7a2", "656d9086c279498fa0af88b28c192b54", "0bb3addbdcec4a7ea4d58fa7ec41e780", "a86198eac03f496f86590ce8ae1aa7c6", "be8c57434a0041eeb2b5a2922d16ff0c", "59dda051061d4a098f673b26a41358f5", "5c911f8db7be466c97d8e7b3608fc48f", "c4b43d7d6ab44b619bde91e0661565de", "79ee5177277048fb808099d0aea19d78", "d6719a3e541041baaf547b96a3b45b9c", "7e410232374e416a9c8856dbd594620d", "f5ebe6ff08a74951905a1ada87b05f43", "656ce2cb8beb4b26baad9fa7bf752eb7", "44236023b7a748ff86fd905a994e230c", "57096e305ac64049adf155420a34c249", "0ad3d7de56c74e8b8b860d902ec2d1c0", "a781839ff0464c48a4f4512520142a9c", "8a8a25d427f042f7afc100e8567699e5", "beb8e8a709554f498ad2756ec1efba33", "5386644d16e0499280280cd62ae41613", "566298f98d524ea5b5dd8344cfefc6c0", "287dbb6354b44bffad7693e8aaba8847", "d3780ed902fc4481b0fe486ffd59f0c8", "e61e56d78c8344b69254e2c1f0e91fe0", "4685a09498ea4c3f8e919df14a085cfe", "a8818500ce374abcac3f2c269326be17", "7891eb0cf2e34bdca1073f663b6eef84", "423e69adf81646b494aaf55e526522f5", "c800d6dc7d574be6a8a621e0a216576a", "981f574f30164a0dbe1fc45f80f67ee1", "e19f7972a3fc44029353e324cae8e658", "1d052eab06064648a244db629bd93562", "a533940a793f47ae98fff307a2b31daf", "ae299b7c61fe418589f637cf99325257", "3bf3de320416478bbbd4de716318bdb9", "20e417a3ebe44faf8246406c12c46b49", "5f83302fc8114506af13bf195806a1ea", "0a994cbcbf90410cbad215866477aa09", "b0a95c66382440478c73b47407c86e1d", "9bbd0997e4274ed29969a31d7bad8021", "faec90fb3bd94a67bdc5333415dd25bf", "42c51d9c36194aa6b57d9c7f8e6dc977", "96ad1e6921d747da8853e3880fab4193", "56c965def6e14798ab22dac2f068bda2", "ac75c01f867c4294845ec6f8d309cba6", "56337fba78b048efbc99722e5bba892d", "7122f1f149214f6986c1ef68d5d68879", "dcb54a1357b94d26b677b9515563d4ac", "d06434ab1a6c4e1fbabf4d07147af6db", "a903c7ad56c74f5fa4d47019b7b1f2cb", "0c7180bef1344298afc76941e999ff5b", "d2c5f317de0a45a7b3b1ce04eead4575", "c5297bf129d0440e8c0e3c59e5d3b011", "d4d806792a0148ffa1dede0f62d16319", "d335807af43d4fe89d12e8d776801b17", "87a979d201044c7d9d4363a6494f7484", "9e8c292a0849468d99655c4cac82597b", "41da00758952419eb37b23ce9dadfadc", "c57972d8c6a44336bdd24f5a2241885e", "09cd202343914300b189c7cca1174c54", "96bf02f0f8854ee6a919cffd53bbbd11", "02695b124a864e118a219bf730b86aa1", "cca800b0f6a04fee823f36372452f41f", "a1cd01bc4d5e4fcab8d343c461baba6a", "d9dbb296b20f4f9fa79388877d575b6e", "30c6cd9708444ee6af2f4f461fce3a9d", "943bd0379a994a7f9396666e5eabf8e3", "4afc2cbb4d5b48989164399929d1bcb1", "18240d3c255342c1a07633828f5b5659", "9572eb96b69b4661a9e73e358141d934", "d6abf4da56e94ad7ab6751bd6bce2666", "13be4df5f44446638c184e50ec137247", "95a5aa343486466aa23151dd18f93655", "2f116cba5afa4b41977c551300fe892a", "0118ac2d1a0c4732a8eff3889aca706b", "7f1694989dd741d0b7e2694b0f6fb94c", "108fe432fa984879a709cef7b649b234", "d8e9f1789f414f00b2d9877df6d12eed", "7e7d53ca58124577b4bea3760785c7ce", "bb6c22e2e012482d88957adc9c1953b6", "8cd5e3a49021453086b2c5e20470bba7", "e8f66f0a293f45f9b4275f44a5ce7fb1", "f1bab036996a401e8fbc459216bd05ae", "b0bb2d3bd7484da896d381132aacfc70", "413bdd64be8f47b7b7302128a68b3a3c", "e9a8954bb3c34680a0e641ccb75b3b90", "c93d3649ebc04628b0a3b0944ee47a08", "255ebdb9960c445ca2aa02d9ad4ef60d", "93679ea57b284d328de6c0984b58732b", "118cf2048a9d4a12836781fc8f87f96a", "6a0bdf0e52634c0298ddf453a5494f2c", "35c466359b924d93b1342a4f907e4dcd", "bf620acb23564cb998951c3b0f080720", "31a6e952d8ba4431b1d65dcab8ce73b3", "86fa8183992242e58c4b349c96c94b2c", "d0d7ab0c19f540dc9b3dcd06ac2afef8", "68fe2f73523b47ea8f07043aab7fc75b", "ba2682c5c1244619a6a525949fded771", "602101eae56b42c9bdf0cba4c08b5db4", "c805b4aa5b424daaad24aad6b9d9c86e", "89812f3a3d194891a3f9ba2e8e5726c5", "a1b0412c1d834f229d2a148f66556ed2", "e4faea58abb84dd78cd3deee43621c7e", "10d269096ef04577935d7766f7d9efac", "8e02478b4aff41ecbebe6b0f4e695972", "28cd142e67354eecae98beeee27e364f", "85d7658f7d6f4d23abf3e96e61f87d20", "aff85219c837445ab85a463d41200bf6", "9413e5b1990f4b11aac96b4354e5edc0", "7312bfa893064fefb8a8dd06528bbd3c", "654b16ef5d4b46fd82851d57fd97b693", "631dcad01f974289b6a37ba518e84dba", "84b4e8f8394b4bfd94a58e2d6a8498e2", "9bf8190c7ca847999c83f14233b8ac61", "9f9fc6ea79bd4afaa8cdca949d543705", "3de3e28ae52242ce912658409789b4cf", "160673a274bd4915b5b7fe189fbb7363", "7a2ff32993b24ae288c6735d1a19caf6", "57000fc336f147328386c9496600b9f3", "b204b85e31594d5a8730f4ff2f476f4b", "fd1b27d6ae964c03993367082e811907", "1f1493955f6340669236b99bbd1205e6", "b9a90750475e49f88baddfed09c2448a", "855db79b3e464f6dab9d8e20a2ba300e" ] }, "id": "R_O04fKxMMT-", "outputId": "2546755e-ec1f-486d-c294-4d260e3be0e0" }, "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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kD-GJtbrdd5t", "outputId": "80e7eec2-86b8-4b91-d2fe-2bed361e17df" }, "outputs": [], "source": [ "fine_tuned_model" ] }, { "cell_type": "markdown", "metadata": { "id": "UObo1-RqaNnT" }, "source": [ "# THE MOMENT OF TRUTH!\n", "\n", "## Use the model in inference mode\n", "\n", "Remember, GPT-4o had an average error of \\$76. \n", "Llama 3.1 base model had an average error of \\$395.72. \n", "This human had an error of \\$127. \n", "\n", "## Caveat\n", "\n", "Keep in mind that prices of goods vary considerably; the model can't predict things like sale prices that it doesn't have any information about." ] }, { "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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jXFBW_5UeEcp", "outputId": "feb4a86f-4a6b-49d2-b97d-7df91e714e9c" }, "outputs": [], "source": [ "extract_price(\"Price is $a fabulous 899.99 or so\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Oj_PzpdFAIMk" }, "outputs": [], "source": [ "# Original prediction function takes the most likely next token\n", "\n", "def model_predict(prompt):\n", " set_seed(42)\n", " inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(\"cuda\")\n", " attention_mask = torch.ones(inputs.shape, device=\"cuda\")\n", " outputs = fine_tuned_model.generate(inputs, attention_mask=attention_mask, max_new_tokens=3, num_return_sequences=1)\n", " response = tokenizer.decode(outputs[0])\n", " return extract_price(response)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Je5dR8QEAI1d" }, "outputs": [], "source": [ "# An improved prediction function takes a weighted average of the top 3 choices\n", "# This code would be more complex if we couldn't take advantage of the fact\n", "# That Llama generates 1 token for any 3 digit number\n", "\n", "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": "lQk7jNlm1oV9" }, "outputs": [], "source": [] }, { "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", " 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