{ "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", "4a8e642ed9ee4d50af215dcf4369b83c", "18c7c7b2774e42e181d8e1976d3c0345", "6d77117f9bc243d0a04d759fb23663fd", "e6037ceb4c6d410db6a31d16156164d9", "efceb7d621184d0aaf489615e887bbd9", "85b54df322124bbe9b756963275c155f", "3d8c388af634496e897f1da032a6a338", "259889c80e3d4ee89dff998048a224bb", "265b15fb78c14610ab0ee4db9df302a0", "97df5652cc0747a8a5a7a7d5e572b43c", "05d992565d0c401d9620858808e5e5e8", "ff60e156992845a7918b6acf696b1d09", "9089249e62cd441887cf20b3603ec79e", "3129251377b34bf897a9addf5d64edf7", "d63a49c1f36d4477ade5d8929dcc2331", "f0ae6846a9394e3b98c6d66a6837f8f7", "470aaf8a510d49389fd613b4ffb2f64c", "4aecf3337ab34ef89ab0a9513a749d67", "1fd080021727490db68af9c5d4251197", "ee7b0f678e8748c1adad27c046f8779a", "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", "4a9161ba495a421181247063da7f3b67", "9814a19b9b7b4348acd98f87f6111319", "acde7103ac204b6cb5af95663b23b1eb", "e5440015bfd846af93e6655bf2f46ea6", "ec2956c1f8574e13b55684721bd57502", "66f48eadf9694cd887068c79a9cb7eb0", "b9461dddabed47cbaef53044006b6409", "56281b4b3d4d42aabe6245b0a495f43f", "6c1289528b27453db677b5b0d06f9e6c", "989af0770a7d434f9356aeb59987d8d0", "f91693d314b949b9bdc3a1912feada52", "459b7c9f03c047e093c4c43f4112332b", "48c5a0d594ac43f98eb9eef72b9298f7", "08b34900369e46f68fcedb9dc4718bb3", "d52214a945f4499588669ba11fd4f1cf", "268c56435b3245c0a3c123af191e23d6", "d4daf40a028a4c0296542f1688f89956", "54146b75c4c744b7b45ab189e399e24e", "307fe52585a94a7cb06798351a9943d7", "c1d907f0ba154e60a9d00f80d2bdffeb", "3636aa81b2dc4effb64e19ee698bc032", "c8fda150be2c4b38ab8afc03d55919a8", "94b0cb362a724ec6a6d1ff8fc588480a", "2168506c06294a8f8a1f6f07ebc743ba", "a66e85714f494754b16eeaf05010429b", "eb67c832162d47d08d477f1918c33bd5", "2e152bc066794346862ed2e80d1eb721", "bc0d68f1fce945b2b6c46842e3297ba0", "1a5bf4a98c9e4a5e86f72b0a531076cc", "d97835fed15b4b529f49f231c6309685", "07583dccef684b2dbd5f3a6ddedfe9b5", "a6090493aaa440fe8eb936aff77eae97", "bbb16875ffa749c2ade2f790638f65d0", "a29fbf488bfc4d22800d3784d189645c", "32ca2bdb43cb4e3288f6c2cb6515abc9", "4070315d5f7c48a9bf46c94f1a9f4dfc", "07fcb7cd29614cfea786d21b74de54dc", "6598e20799464c708663e4e2dc21ce05", "14eb6f1686af4e1080ee6dab45c60f6b", "4a7548dda6eb44ee9e60922d35c2142e", "e6a3796e5e2c4b3ca472ffea660574c8", "f92341b6c4f44f82a563a67f064f88fe", "aa7957cd501e4ea5869c116b292ea459", "31f3a05b4bf2471e95e67fda87e857c8", "06c4104cc2b442a49a2a96c13cfca3dc", "1816cc74640c447f8a6f95ba336cb1b4", "27e9abe7a7b84f93846db8af9b4430fb", "08793171cd034ab4a9c90321b87aefd8", "09c67282d3d74a319dd2218877b5c81b", "711193a8932f4fefb4202e748c5f898a", "934438dfeaaa439bab4767e4d9c933da", "2b431820110f413ca3579468f0eac1a9", "0c4e24553b164e04b68bddf7f95d28cc", "ce7a1f29f0ee432aa7d237db41a5ab60", "7af36d566b034878a35a59942b5b2027", "d56ba22172c8480e8d41339d21b6847a", "a9c665f30ea44e4c9da40f1bd3d7005f", "74b5af33fe6e4b98b57f98f3a75db74c", "d76daac11060462084a3d452e3390dcc", "ced7bdc62b0245938997b0f3cd54458f", "835fc18fe79040558c399bb841d040aa", "b711cfeebfe349538759e0cdaf9458a1", "46e8b46e94624cec8d2be697e86022e7", "ee8a956c17bc49eca1998de7a308670d", "0afedd3e20164e1db49c59ae19177a34", "e7a08cb692994676975dc402eac3cdf0", "312fa9f55d0e4eca9103c2f26fee819b", "76728b118e584cdf9b0f32b67916a0a9", "d5d7099256814dbda579827289b25118", "aed87675539f4a7f9cca4cbfc9d2bf64", "a85f333318f84961abbfc6ca515aaf39", "7a3bfc1e5f3b40adaa50ab04bda2be2a", "e38ac43049ec4fdaa09a557c37b6cbb2", "a9c9ff0e07dc44348fb51e238e72a971", "dc6010df3e3d474eb73528846d389cde", "16a9fab752724937a748f85464e30834", "33fdd8b415ee48349ee49ce906c5910b", "f6a606608b674cb6a222546a95ed01fb", "ad72a1c78e8b47289f6052f76cda7ee7", "58009c77ae5e4501a51611b4e4920397", "7baf2cd064894596a3dc4b42949db7f4", "173713283ebd4ea091610f1d4c657b11", "a6eabc24d9e8439495d61b220b587e57", "e50d7f30245e4094b64dcddd69944e2d", "32e6424270a74b97a055b8a8f11e1019", "c7a766b6d87b4503bcd02f8969787a5e", "7219dcf1529944c0bc8fcfae4e73c9ba", "b74b4233f230497f9f26ba0ef7d46928", "d4aa469959c949e2b144b6df1d0c8d2e", "5779dbc5149f4f45bf3c9575511051b5", "4617523da81a46a1aaee11a6a52bff04", "d84d057e7e394987b2db9ed7c90919d6", "9c77a24fc9744202a91d0114ef96de7a", "d88b2186d1c84b558ba29daf9bc17989", "043af5486c4d415fa3eb784ac6c83246", "30af83b6f04149e48c01a9b04d498893", "9f026d33e4de41be88a9d0ce840ec9d4", "a37577cb31f249a5961cb5ccba7abd2d", "d6d3dfb72f234969abcbad023ff03f56", "c5736e6bf5f34e92a96b83ee18b49805", "63f085263b98476ca2a5b3ffa9fe5e87", "a809f4b5d1ec4651b90941e0d940df56", "0c6edf370dc04a76b96c5b9e1bb8cbe3", "4a94a63f4e6c48b2af23a0023a27699f", "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": [ "0ee79c2ced664059bb61029efe9b025c", "961bb9bf6c424965a3c1ce762672005a", "eb02482836674849ab4879296aebfdc2", "d9aecdc75b304ab381ef9b688cf5468e", "2db8a634b4cf4c4c87428513f209398b", "3b1919a9e6934ad49ee3141c8594cbfe", "53d51ebd9bff4af1a0c67201480f8af8", "8bcb55a1416a421dac6e06d45c18872a", "710f77ab372e4cc680f61b165518bc2c", "229b988a9e384cecaf7304685c453f2f", "0c90c196c58d45cca22c37f8b0d977ca", "89271f0de207444281281f2a1bc03a8a", "e32e185817ca4d53b7fe258a88ca3324", "8a85944747454e40b5877ae5bb784b03", "fcb789d5cd584eaaad97de99a6622859", "592946d3e0b44a78ab6d35afd8663fca", "875caac5428241caaa2a4c113541756f", "840cc942b47b449380458a701c05bc1b", "bac7cc12dcd443059382bb7b6b2cb79c", "98673672e445411d89d40af431770bc1", "29f9369c5f82430f84224d42f06a97ab", "b233caa5368544dc8d4434dbf3643a92", "abdaf7ae011043f6b7231f29b9fbfdec", "2f961bd3e3e446a79e05309d6b68c726", "4950b100ee024fd7960b6e311a2c0887", "4e94f9fb2b554efaa9c5e7f9c3c64d74", "b6a5ca6444a245a2b5a81f7bb896279e", "22711929aa7b4b33a42a7ba2f0b38068", "be70edb1837c43c48f1e31631ecd6b4f", "6b42a19f83f340fea69fd0c25a001e3b", "030b586f3eb74ff2be38c7d4b14841f9", "88cc054dc5cf4682a381013738a4fe6f", "7e3ff775c4df45dfa4e3ed5080d281a2", "2cd3674c316c4cc4ba36b5ccfbe16258", "e56365f065594865b7c4874bb0af3c53", "032ea2655c594c96b33024228a176f0f", "ff2e6fa0335848e29819349d04e24733", "1e18b45ddceb45c2ad63e346e77cb8ff", "ae968072f4c64a688c86701b752f5cba", "676fc027944b4bee9d3e16fdf47e29bc", "46ac9df4f4df4e6f9473a76f6d6efecc", "d56556ee703844d7aeef4d1aee0aca88", "eb2e3a14934543ecbb8d8bbb951a6b97", "78dfed2b36e244b7a4aebaf7110a19b2" ] }, "id": "GfvAxnXPvB7w", "outputId": "d61e240c-078d-4f0f-9e6f-4b3be9137d21" }, "outputs": [], "source": [ "# Fine-tune!\n", "fine_tuning.train()\n", "\n", "# Push our fine-tuned model to Hugging Face\n", "fine_tuning.model.push_to_hub(PROJECT_RUN_NAME, private=True)\n", "print(f\"Saved to the hub: {PROJECT_RUN_NAME}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 596 }, "id": "32vvrYRVAUNg", "outputId": 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