diff --git a/week7/community_contributions/muthama/week7_exercise_Testing.ipynb b/week7/community_contributions/muthama/week7_exercise_Testing.ipynb deleted file mode 100644 index 964fe88..0000000 --- a/week7/community_contributions/muthama/week7_exercise_Testing.ipynb +++ /dev/null @@ -1,7070 +0,0 @@ -{ - "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", - 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"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", - 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"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", - "29b938d6ca7d437a829a60f907eff8c3", - "a356a53a81914f08a29d3adafd42def1", - "a111659991064097a54e20f1572d892d", - "c3754b1a8f134051b3b58636bb26a98a", - "7b3844399cb24f57b36f4ca95d532e3a", - "9136ab0ffa1f4356a4f94ce4dd258140", - 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"\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": { - 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] - }, - "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": [ - "0ee79c2ced664059bb61029efe9b025c", - "961bb9bf6c424965a3c1ce762672005a", - "eb02482836674849ab4879296aebfdc2", - "d9aecdc75b304ab381ef9b688cf5468e", - "2db8a634b4cf4c4c87428513f209398b", - "3b1919a9e6934ad49ee3141c8594cbfe", - "53d51ebd9bff4af1a0c67201480f8af8", - "8bcb55a1416a421dac6e06d45c18872a", - "710f77ab372e4cc680f61b165518bc2c", - "229b988a9e384cecaf7304685c453f2f", - "0c90c196c58d45cca22c37f8b0d977ca", - "89271f0de207444281281f2a1bc03a8a", - "e32e185817ca4d53b7fe258a88ca3324", - "8a85944747454e40b5877ae5bb784b03", - "fcb789d5cd584eaaad97de99a6622859", - 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