diff --git a/week7/community_contributions/muthama/week7_exercise_Colab_Results.md b/week7/community_contributions/muthama/week7_exercise_Colab_Results.md new file mode 100644 index 0000000..663e7d2 --- /dev/null +++ b/week7/community_contributions/muthama/week7_exercise_Colab_Results.md @@ -0,0 +1,3 @@ +### Link + +https://colab.research.google.com/drive/1CAF3VcNvZs3Eex71TMX3A5exw2Bw4HUF?usp=sharing \ No newline at end of file diff --git a/week7/community_contributions/muthama/week7_exercise_Testing.ipynb b/week7/community_contributions/muthama/week7_exercise_Testing.ipynb new file mode 100644 index 0000000..964fe88 --- /dev/null +++ b/week7/community_contributions/muthama/week7_exercise_Testing.ipynb @@ -0,0 +1,7070 @@ +{ + "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 + }, + 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100644 index 0000000..4d20341 --- /dev/null +++ b/week7/community_contributions/muthama/week7_exercise_Training.ipynb @@ -0,0 +1,8132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "GHsssBgWM_l0" + }, + "source": [ + "# Predict Product Prices\n", + "\n", + "## Training!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MDyR63OTNUJ6", + "outputId": "64fdae64-b76c-4295-bc60-2ff83b27c0fc" + }, + "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", + "import wandb\n", + "import torch\n", + "from tqdm import tqdm\n", + "from google.colab import userdata\n", + "from huggingface_hub import login\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, set_seed, BitsAndBytesConfig\n", + "from datasets import load_dataset, Dataset, DatasetDict\n", + "from peft import LoraConfig\n", + "from trl import SFTTrainer, SFTConfig\n", + "from datetime import datetime\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 = \"muthash\"\n", + "\n", + "# Data\n", + "\n", + "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n", + "MAX_SEQUENCE_LENGTH = 182\n", + "\n", + "# Run name for saving the model in the hub\n", + "RUN_NAME = f\"{datetime.now():%Y-%m-%d_%H.%M.%S}\"\n", + "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n", + "HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n", + "\n", + "# Hyperparameters for QLoRA\n", + "\n", + "LORA_R = 32\n", + "LORA_ALPHA = 64\n", + "TARGET_MODULES = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"]\n", + "LORA_DROPOUT = 0.1\n", + "QUANT_4_BIT = True\n", + "\n", + "# Hyperparameters for Training\n", + "\n", + "EPOCHS = 1 # you can do more epochs if you wish, but only 1 is needed - more is probably overkill\n", + "BATCH_SIZE = 16 # on an A100 box this can go up to 16\n", + "GRADIENT_ACCUMULATION_STEPS = 1\n", + "LEARNING_RATE = 3e-4\n", + "LR_SCHEDULER_TYPE = 'cosine'\n", + "WARMUP_RATIO = 0.06\n", + "OPTIMIZER = \"paged_adamw_32bit\"\n", + "\n", + "# Admin config - note that SAVE_STEPS is how often it will upload to the hub\n", + "# I've changed this from 5000 to 2000 so that you get more frequent saves\n", + "\n", + "STEPS = 50\n", + "SAVE_STEPS = 2000\n", + "LOG_TO_WANDB = True\n", + "\n", + "%matplotlib inline" + ] + }, + { + "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)\n", + "\n", + "# Log in to Weights & Biases\n", + "wandb_api_key = userdata.get('WANDB_API_KEY')\n", + "os.environ[\"WANDB_API_KEY\"] = wandb_api_key\n", + "wandb.login()\n", + "\n", + "# Configure Weights & Biases to record against our project\n", + "os.environ[\"WANDB_PROJECT\"] = PROJECT_NAME\n", + "os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\" if LOG_TO_WANDB else \"end\"\n", + "os.environ[\"WANDB_WATCH\"] = \"gradients\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "4c846ad1d02345069b7bf0ff2825c1ec", + 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"ea878deefa064a6c9812d1ce4b2dfbd4", + "6b3e148a46344e9cbe52fc5325f127e9", + "b3bf2f2aba0c463091b14e81ec839c27", + "05953b14a75547af915cc8aa6df73a4c", + "22158805f4c0424aafb485dca3afc2c6", + "e40569e6e3dd4552aa97e79943034926", + "596a724b0bf444afa2df0a2f325bc65c", + "efc0f8e3f4b9493296d1c0af9a00c78c", + "b8203ffc3bcc4f2c943ad8d3f7e9f9f4", + "a9c174734a9a4b979a050550c94ba45d", + "89554451afb34ea0ae2c8175d90a1a10", + "936887ab43a8486682127aaa05182232", + "cff6ee289b8047d989e78702f84bdb29", + "c17781f031dd4044b6fd3044940e0c4c", + "0daf077d76df4bac9175d4cad4b3f088", + "c5530f630e8f4a37a43c1aa4c43bcfde", + "f3eb63f55d434b4eb377ac494ba271c3", + "db69c7477e6743bfae49530feb0b004e", + "b0c43a04f85040af937435c03d2ec7de", + "fcbd65b8af804b828f344481953e02f1", + "3137db33433a411db5412b6001fe006f", + "021b7ad1b42548149aabd2aa740442ea", + "c2820a449ff141608ae4561f14a633bc", + "c4e20fbf4bf044f7a052c6174dad8efa", + "b36ce8e6f29d469eaa970510b2099117", + "c526fdeee93d41ebad5dbd8c3e3d4211", + "e354c5e32f6049579776a667058e93c0", + "959b9403943c49bf8eea15ed57077c5c" + ] + }, + "id": "cvXVoJH8LS6u", + "outputId": "4c0d9ede-7650-440f-b1c3-30367405c56b" + }, + "outputs": [], + "source": [ + "dataset = load_dataset(DATASET_NAME)\n", + "train = dataset['train']\n", + "test = dataset['test']\n", + "\n", + "train = train.select(range(20000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 121 + }, + "id": "8_SUsKqA23Gc", + "outputId": "90c72a1d-ac2e-4ce2-9772-2540a7eef3a7" + }, + "outputs": [], + "source": [ + "if LOG_TO_WANDB:\n", + " wandb.init(project=PROJECT_NAME, name=RUN_NAME)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9lb7M9xn46wx" + }, + "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": 418, + "referenced_widgets": [ + "07ad36d277f0422494506209792bc8aa", + "67888f7d828c46d295d480daff27f7c1", + "d91ce8614e7944c6aacbe6e4a11ccead", + "dfe449c7d61b441c9467debfd0ec797d", + "8164289c275d4e128696f9c33cacd279", + "b49b503de9e64132bd22357a0a2f598f", + "b2ed1fcfc522419c878921e7850db301", + "561d8cbe415d4bdf8bc2d7ce91ad9314", + "4302b3e38ccc49c0a4c965259a068f7c", + "130e273360a14da1841a1c87787024be", + "135b65b15f3240c39a8b795349799887", + "c5e9fba90838464aacc8f6996d759b18", + "912779ac6ca84078861fc0541f5cd6e9", + "4a29b53eac554af3aec3c46f18588b06", + "53b6165d72494e7aa8b9d3ddd63ed5be", + 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"def877b1abba41de8a8f8482f494d3b5", + "e45693e9a7dc431fa228e47e86ff59fc", + "52eee64e6859463c8fdb26978bc29432", + "ad4dd9ed9f6d42d0bd374a77772714c4", + "6a2afab4290e4161b2f63103fe524371", + "236d319c0fee41a488753701b9b5afff", + "6a13576a04a5470aa4c5ec07fa4bf6fb", + "2211164dbfd340abab303fa736cfd902", + "27c2fc15b142493ea66484519df18b1e", + "97affb84d8a64f14a0c6e5abf3eeffea", + "b36675b246014e9c9c64719d0e0fae12", + "c9597b6af8ac4a5eaab63d31f7a734c2", + "fb7cbcf5074c45a19ee9d24efc1751fb" + ] + }, + "id": "R_O04fKxMMT-", + "outputId": "c2e450e1-a090-431e-ca79-a585df269531" + }, + "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", + "print(f\"Memory footprint: {base_model.get_memory_footprint() / 1e6:.1f} MB\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2omVEaPIVJZa" + }, + "outputs": [], + "source": [ + "from trl import DataCollatorForCompletionOnlyLM\n", + "response_template = \"Price is $\"\n", + "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "f477da9bc5d1478db4897c8325ec8704", + "edeaf1c7918e4ecd8f34fab2ddb0250f", + "5c9a914026d941f3be58a0c357927bd0", + "0ee36bff5d2b4961aabde0b69f0cbe7e", + "8e9e763fe52c44f8ba25e2cbc98ff171", + "50fbc5cd166c449d88857ce0eb941961", + "dd124d780e5d40d29c27e218147d8212", + "7eb275b3d3424316b1def46694135db7", + "7d3cdcb126984f11965df367f33c9196", + "86308c508f1e4ea2840608e7fdd1c73a", + "de578ce075e44e83ab016fadf671144c" + ] + }, + "id": "fCwmDmkSATvj", + "outputId": "87d1e492-4de6-4d5c-b966-97f8a4a7d75c" + }, + "outputs": [], + "source": [ + "# First, specify the configuration parameters for LoRA\n", + "\n", + "lora_parameters = LoraConfig(\n", + " lora_alpha=LORA_ALPHA,\n", + " lora_dropout=LORA_DROPOUT,\n", + " r=LORA_R,\n", + " bias=\"none\",\n", + " task_type=\"CAUSAL_LM\",\n", + " target_modules=TARGET_MODULES,\n", + ")\n", + "\n", + "# Next, specify the general configuration parameters for training\n", + "\n", + "train_parameters = SFTConfig(\n", + " output_dir=PROJECT_RUN_NAME,\n", + " num_train_epochs=EPOCHS,\n", + " per_device_train_batch_size=BATCH_SIZE,\n", + " per_device_eval_batch_size=1,\n", + " eval_strategy=\"no\",\n", + " gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n", + " optim=OPTIMIZER,\n", + " save_steps=SAVE_STEPS,\n", + " save_total_limit=10,\n", + " logging_steps=STEPS,\n", + " learning_rate=LEARNING_RATE,\n", + " weight_decay=0.001,\n", + " fp16=False,\n", + " bf16=True,\n", + " max_grad_norm=0.3,\n", + " max_steps=-1,\n", + " warmup_ratio=WARMUP_RATIO,\n", + " group_by_length=True,\n", + " lr_scheduler_type=LR_SCHEDULER_TYPE,\n", + " report_to=\"wandb\" if LOG_TO_WANDB else None,\n", + " run_name=RUN_NAME,\n", + " max_seq_length=MAX_SEQUENCE_LENGTH,\n", + " dataset_text_field=\"text\",\n", + " save_strategy=\"steps\",\n", + " hub_strategy=\"every_save\",\n", + " push_to_hub=True,\n", + " hub_model_id=HUB_MODEL_NAME,\n", + " hub_private_repo=True\n", + ")\n", + "\n", + "fine_tuning = SFTTrainer(\n", + " model=base_model,\n", + " train_dataset=train,\n", + " peft_config=lora_parameters,\n", + " args=train_parameters,\n", + " data_collator=collator\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": 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the hub: {PROJECT_RUN_NAME}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 596 + }, + "id": "32vvrYRVAUNg", + "outputId": "c5c5a957-b212-4298-9355-e60cb812fabd" + }, + "outputs": [], + "source": [ + "if LOG_TO_WANDB:\n", + " wandb.finish()" + ] + } + ], + "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": { + "021b7ad1b42548149aabd2aa740442ea": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + 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