From 5e36d69af667709663eed81ecadf7e0dd22a30db Mon Sep 17 00:00:00 2001 From: Chimwemwe Kachaje Date: Tue, 28 Oct 2025 06:10:25 +0200 Subject: [PATCH] Week7 exercise Andela GenAI Bootcamp --- .../kachaje-andela-genai-bootcamp/.gitignore | 5 + .../week7-exercise.ipynb | 7293 +++++++++++++++++ 2 files changed, 7298 insertions(+) create mode 100644 week7/community_contributions/kachaje-andela-genai-bootcamp/.gitignore create mode 100644 week7/community_contributions/kachaje-andela-genai-bootcamp/week7-exercise.ipynb diff --git a/week7/community_contributions/kachaje-andela-genai-bootcamp/.gitignore b/week7/community_contributions/kachaje-andela-genai-bootcamp/.gitignore new file mode 100644 index 0000000..ab03894 --- /dev/null +++ b/week7/community_contributions/kachaje-andela-genai-bootcamp/.gitignore @@ -0,0 +1,5 @@ +items.py +loaders.py +testing.py +*.pkl +.tmp* \ No newline at end of file diff --git a/week7/community_contributions/kachaje-andela-genai-bootcamp/week7-exercise.ipynb b/week7/community_contributions/kachaje-andela-genai-bootcamp/week7-exercise.ipynb new file mode 100644 index 0000000..bc4aa90 --- /dev/null +++ b/week7/community_contributions/kachaje-andela-genai-bootcamp/week7-exercise.ipynb @@ -0,0 +1,7293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "xhF_8Gy3BkkJ" + }, + "source": [ + "## Predict Product Prices" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 268402, + "status": "ok", + "timestamp": 1761625271540, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "VB1OA8NQBen3", + "outputId": "17d24790-e768-418c-b0c6-ea4b2d2b6371" + }, + "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": { + "executionInfo": { + "elapsed": 23322, + "status": "ok", + "timestamp": 1761625303538, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "3czhPr1DCAmh" + }, + "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": { + "executionInfo": { + "elapsed": 16, + "status": "ok", + "timestamp": 1761625310250, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "7148hFC8CMLK" + }, + "outputs": [], + "source": [ + "# Constants\n", + "\n", + "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n", + "PROJECT_NAME = \"pricer\"\n", + "HF_USER = \"ed-donner\" # your HF name here! Or use mine if you just want to reproduce my results.\n", + "\n", + "# The run itself\n", + "\n", + "RUN_NAME = \"2024-09-13_13.04.39\"\n", + "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n", + "REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\" # 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": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 1218, + "status": "ok", + "timestamp": 1761625315056, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "sFFKEgn9Cbh_" + }, + "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": [ + "95ba6ae1ff2840c6800de4a80fc8d5b9", + "f503c22b0b6b4822a214c9cc2b4edfc1", + "d68f0077dbd5449aa9d482cb5dde0dfc", + "9c348815cd504a9aa77a2f5fcab68ad6", + "eeed94aea8c3426b841f3c2bb50b0edd", + "530629a3e38142e39cc62c398ea0c2f0", + "d39283c0b0ce4e49a0b37f044791a49e", + "7db8db2cdcb541cebb1ebc87c28e7551", + "6e7eafe5512e4275b6bb26e20b48ec29", + "470b2793ebf54ff68b6d5f44c4788054", + "e31f473c55fa44d28ace0bf64f9d8e97", + "2f919253d7db40438b9f213f4f4a4337", + "e7a769b9d2d1479d8da3d973e534c71f", + "33a0d53428bb451ba860400f0d797f4a", + "90cd1dac89cc4499b0fc25c9efa49721", + "6142f58521524d229ab31344baff8147", + "845435c634f04e62afca2b28ed4698bd", + "f7ce6ebcd3d44b8c86065dca74fd1b99", + "d01e6749e06a4e0a89d54a62b2a054b4", + "7a2b4ef5f2d340fc95ad068a133dbb2a", + "c389e8ea21684aee9b0eb8806c3ee335", + "23f0b9324bc74e06b3f723f950aeab7f", + "b96411d1252c48af850f9ebd7f7dd97c", + "5e6c101c51a74b5bbe1c4b6609708fe2", + "6d59fb7aa3414f22b92b2628fe2f7546", + "de58d60f1ca441ea85f60396267660b8", + "5223f265df7945d7805766a8f84ebc97", + "7f66101690484a44aa5684a7bbd94d41", + "9269522a5dd8422cbd4aedec2c2f9b70", + "30fda3df2305435caa9a3dd9b0683c1a", + "b8a0b31a99764e828c59e0895ddeedde", + "85456988a9654bf39f1cda856c4a0ab7", + "61152130cd20459bb8b5ae7eedcb1c18", + "e7e7aac5de6c4b8bb93a3605d0f1036d", + "0d0c011c87c84399a3342cceeeb78d30", + "90d1ab3dea18409f8dbbc3fac66d1549", + "30befb09ad704e3b89191a8a30a62c64", + "daded096bff849fca69fd55e5aef9ded", + "c3ad0f5aaa6548c6846df32aef244ea0", + "45c9d4cf05b849058c2adc1d198dc82a", + "6f12c4143f184db7bbb9191f5aa0dc01", + "ee132c43cca643c0bba72e8ff556d035", + "4c7d4ee204474487a6e38a1e2ed83836", + "64963a6c7f17479b8bca6229b6a0330a", + "57dae4c6f85a4d78b125677207f4eaa5", + "58e7f187956e415795ab72d0d8e80f7e", + "837091dbeb004a59bec233ad8776f48e", + "d65c29f7442d4f2ca8b73147f2047083", + "76e205551f984b5da593c4da0a64cdf9", + "7509c39b3c5449319f0b63e5659c3d88", + "5269c752096644f5b68cc69d419eaebd", + "f717007740be4394b57fd4bb1219f8b2", + "4820c62692e5420da4c3d6709a67f53f", + "75a0523565b144b49a87c301adaadcdf", + "cfbaf4753abc4176af2177462aa5a46e" + ] + }, + "executionInfo": { + "elapsed": 11900, + "status": "ok", + "timestamp": 1761625329031, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "8ykpbUcoCfcg", + "outputId": "2cf8df60-dc4c-4030-b0f1-bd271e728ca0" + }, + "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/", + "height": 144 + }, + "executionInfo": { + "elapsed": 57, + "status": "error", + "timestamp": 1761630593442, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "wJzJ2xxQCnFq", + "outputId": "6515252d-4310-4d87-81ea-74decff164b6" + }, + "outputs": [], + "source": [ + "test[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1761625337005, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "bV_Ns-b1CxVb" + }, + "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": 574, + "referenced_widgets": [ + "2199a0f248f842d0bcb0120c7e585de7", + "0dba92a4c3dd45379317876fc962974d", + "77c1f8b104484765911ae2739a93f35f", + "abe3c5eab7ed4e568dcda77dc9fa383b", + "c211b88a76df41d78af94846fc37ce92", + "5bc8d20326284e4ebd5585c37ecb2555", + "c50102f380bd43b5a823b4f668c4e2d8", + "c9176622489f47b580f649c446a30f9d", + "62ab9642d96a42f9baf3905d90764d75", + "5536dc83a0ca496f9ecf395b1a59b80b", + "2ceaf209c3344caaaba340fe54715c1a", + "5228a232ddb24f3f9cce50d7892791f0", + "700a8c496abd4234a5a666dc876841fa", + "a16d8d026e9c417489a7aa9a6f5d5171", + "cd97a5aa438a41e7b99f8782841ad489", + "4a514d9867434716bbcbf74b41165135", + "0a0c9bab552742cca7baa2f0853b96e5", + "98acaebfefe84506b1b1e4c69ce01663", + "f82e9d5c771b4125803f102fcbf43fda", + "b797e581687d44018a0206b6c241b579", + "8c209e7595be4e4ea673e11a5714b44a", + "67eb51fbc64648feb6dfa092416917a6", + "046ba2cbc2334147a1ad1b198578186c", + "e71c0b89737d46cd819ec55ff31cbbda", + "32e3116acd814a5cb5a270a251ffeef6", + "d1e66c01f37d4a02a046575d6882929b", + "f28822d8f73c40eaba31378e55246930", + "bf8b36c40cd8402892a1350288fa6670", + "2ddaf05f86b04e619c7d4266dbda088d", + "36a9f307c0e7491f95d1d1929de6521f", + "78f8b3b979a243879febc11c079dcd1d", + "0607f006f05a4fd38c8ed0b2dea3a56b", + "15e5b4cebcc844c6801cc3321699bf2b", + "eae80b11f4f64b76ae02f7ed3fcdb40a", + "23d065415c3a456d85be0d55126ca00c", + "d39187f349b64d54a9fe165ba65e4410", + "165ba4141e2048ffb9e5df3d070de6ec", + "ad470938d7034037b083bb1b6ff9ad14", + "214b44e778d34dd1ab4a9fff5a578af4", + "c0b77b8ddce946408b54ed637af2bfdf", + "65c44f64a2b04b85a3af8b48628d8810", + "6b5d85c791bd4f0d9ee03ed95da3fc9a", + "9d467b63c2d34de8ae03e171bb64ff66", + "9daaf46f885a4499a65fc3b45b632665", + "2d7398ebd5da42a6a4bac781c8e0cfd1", + "3930d2fa80ba4c35b0e97fea43021ea2", + "26a9b81c43104a97866b8c2c56b6101d", + "106f0e5fdc354550aab388da6af9bd47", + "e91fb3b7fc274be5b6dae1291120033d", + "626e0354a8e241d3b3fd242a0949b00d", + "3edda2f2327d4442b65351113454e374", + "95d336e9e66b40cdb9870cec496f1111", + "546e8cb29864452b94d3d8b5dfc7c839", + "0a85e671f6434eaeb6317c558bd238dd", + "c36e95e1c69f4441a02acf9aa07ccf11", + "cdb73a111125452ba829795897b470e9", + "ca9f48b2a2094044a625f89d9a546dcb", + "664df90daea145a38f27644180278fec", + "e6bf20dba5784da89b828cebdfd66dc4", + "f852514e1c65481284fe05bdc39d6206", + "43df13c84c5c483e82032a2b17d07046", + "560218aa99c341e79f3f1164cbfa6dde", + "bc3cd4db4e5e448298a7b728216cc401", + "384853ec2fd84c209060e04aa95a248c", + "86235cb3ca3e45d7be053def0fe078e2", + "d599ff0236f1478b8d24da946b8bb693", + "24cd75ba173e4820952be5750d7f0902", + "fd72a7952e0b4490bb709bf2c3b35caf", + "8024b550d25e434285882accf792c569", + "005d01271deb428aae9938eebea80604", + "c3e40a74c0cc4ecdae0e312b052a2a67", + "254652c3bbb14846a05d487045ed57e7", + "5f17c81085364f01a3300c885dd3b0b8", + "4afd7212ec03482fbb0405538648cb4f", + "a5919afcbcf445d9a8f78922a9db9723", + "636b1d72762a416ba9d9a78bfdb510e0", + "5150b62c1644409696a98299ac9822e2", + "f41e4d3887624f6a92406c06a44b79ef", + "ac5b7a7903094b3ea6b6d87f5edd1744", + "866df8a2b5ca4a8b9df04a5f6a1fcc01", + "bec9bcc37d4a4cff9e491339636527f3", + "01b7e316f0184eafb9c456ce16e17f68", + "1d409341e0654bb1a391ab9ced4bf5aa", + "b30b1e85c01849669077a7173c3b983d", + "0ffad90443244508866d5be8f1efed52", + "45567d44ed9c4f6b8dea919ee23090ba", + "4c9f076a9b0843ed97e342cb8904dd05", + "4cf1f920468c42689ba3c0fba56927e1", + "9e8e01fd73ed4f68868beb05818a68dc", + "3cd354fc4935464ca38136b9a4d5e0bc", + "c75a96755a254c059657fb411f753480", + "8eef7516cb0d432ba5ebe30585adcceb", + "1ffeb3d7b830478292795b21110102bc", + "c6713ed1505645ae855f43a8e2adff3a", + "9596bd06e0404061b2438e9427d0dd64", + "f8746f68003f493ba46599ee8b1964c8", + "c9016dc20c6d4b48abd8129ff9d01680", + "a5abe819e82b4e80b53e4e0eab540aca", + "b78ddb21f805477e8beae9af6613fd12", + "ccea944c62ba4a1b8040414cf57b1e89", + "ca6015de1c794ae281abe1364dcd4e8a", + "233b90a4b3054693ba1c45d6ecd55889", + "00350ca801c2415cbac5980e66ad7b13", + "52ae0cd1e74b4f3f9e2f620a5b06b678", + "61e38a95b4134c1fbf925555955e1198", + "47788b9d601146d3bd5f187b701b3052", + "c8e762acf32248da9b08f56b5a46190a", + "238ca37ba65e4430a1e1887c672cdd04", + "255f32d72794483fa6430f1472f91b1f", + "2442ad9a4cc04fb4adbe0cb1ef8be0a2", + "17595f058ef6443aae21943b5876529a", + "f4773d60482c4d30ab23ebcc8a39d91a", + "2040087360f04d8a8e56d26f8a6022b6", + "87f68f7d4647430e84dc5b7209a69bcb", + "96484444bb054e1cac73f559c9a7905f", + "bd496932944942439e3d53d2be0f865b", + "ad4c211d5a254ba89db8375461d7205e", + "95adbc92cfa64179b19b9f4b3b75e6c2", + "070a3b0aad79435b9ce731a61e1cbcbe", + "24cf09cfddbb4af2bb5523dcf480103f", + "1ea03ac9729e4a1d9a08a8b2f1340dca", + "f0da0a530a5a4b56b83019963b19c98d", + "26b38506c8c048fdb896ad084b9827bf", + "5c53bb5124b244139e1a4ec2578e7d00", + "74970c9c060d4efbb314ea83f6caa907", + "1639426f4a064c5790c89beec4992df5", + "0438c8be86164bfd87233253eb3c63ec", + "ffd42f70c60645a7b84d069d198155ff", + "05631b93ae734374b406c51c6bb74610", + "28cefca88a9a4dfba1fa9039624cad61", + "9bec5570569b48229702e02d5354e163", + "fe97888b0f654f4a9d110e70da64eef0", + "513f8c0b63c54a43b6dc0163327206ef", + "2df03d1bd86d43c68d1fe0990e1971c9", + "85dcbe07b31e47e8868acb9879a0255b", + "5e2a53c94a2e4e51a0d576b19fadc958", + "c0a19ba38a8b4e278e4a5bfd79ac355f", + "f59ed072c8f3415784a432c8b278cf2e", + "4ef312dd299542fe84c3c185f09b3436", + "700feb5d8e4d4896a3a4ad9a7b37cfb6", + "f776f7c770984c1b93432a5324efc937", + "cdb11e380905470baf0a5a092ef716cb", + "a64b6f5ed29a424c9a54edc824766083", + "051d93434e8a404c9f28ef6f7eb38799", + "c4f3d8cd92b94382acbbeee5ac00f848", + "eec3cb13b7b8481fb14539f6c6092607", + "27a04c815f06483192943878bae78175", + "9a6b6bdb746240389b4f74a89c724486", + "69d2b1c14bcc4f6cb75d3adfbf96bfc9", + "6f3bf16212d14eb2a2fe11a0db837dbc", + "6ae5625c47cd4493952a190c22f2e495", + "00e3cbc5332249c2a15fcc6a233ae7cb", + "80559b9611e2417a8c569357fc03cafb", + "f0ee873f2e3b475fb23d6420f5c52668" + ] + }, + "executionInfo": { + "elapsed": 1468968, + "status": "ok", + "timestamp": 1761626809118, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "YYZMohOuCySf", + "outputId": "d0c304b8-39f0-40a3-9e38-feb157a02056" + }, + "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/" + }, + "executionInfo": { + "elapsed": 18, + "status": "ok", + "timestamp": 1761626809146, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "Sn-aZH-0C2QZ", + "outputId": "32950052-f2b5-40a7-8382-f6f8c00f7f9c" + }, + "outputs": [], + "source": [ + "fine_tuned_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1761626809147, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "jmfwNBHNC8wC" + }, + "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/" + }, + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1761626809151, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "uDk8XkaCC-pp", + "outputId": "74f196a1-69af-4d60-d399-d3468601d9a2" + }, + "outputs": [], + "source": [ + "extract_price(\"Price is $a fabulous 899.99 or so\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 1, + "status": "ok", + "timestamp": 1761626809155, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "bVx0jycMDBmD" + }, + "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": { + "executionInfo": { + "elapsed": 2, + "status": "ok", + "timestamp": 1761626809160, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "CTCbhJ2xDCiu" + }, + "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": { + "executionInfo": { + "elapsed": 2, + "status": "ok", + "timestamp": 1761626809163, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + "user_tz": -120 + }, + "id": "YMQMTZECDHrh" + }, + "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 + }, + "executionInfo": { + "elapsed": 364087, + "status": "ok", + "timestamp": 1761627173251, + "user": { + "displayName": "Chimwemwe Kachaje", + "userId": "01022304238626387479" + }, + 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