8133 lines
289 KiB
Plaintext
8133 lines
289 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "GHsssBgWM_l0"
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},
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"source": [
|
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"# Predict Product Prices\n",
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"\n",
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"## Training!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
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||
},
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"id": "MDyR63OTNUJ6",
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||
"outputId": "64fdae64-b76c-4295-bc60-2ff83b27c0fc"
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},
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||
"outputs": [],
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"source": [
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"# pip installs\n",
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"\n",
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"!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",
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"!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"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
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"id": "-yikV8pRBer9"
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},
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"outputs": [],
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"source": [
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"# imports\n",
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"import wandb\n",
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"import torch\n",
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"from tqdm import tqdm\n",
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"from google.colab import userdata\n",
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"from huggingface_hub import login\n",
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"from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, set_seed, BitsAndBytesConfig\n",
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"from datasets import load_dataset, Dataset, DatasetDict\n",
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"from peft import LoraConfig\n",
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"from trl import SFTTrainer, SFTConfig\n",
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"from datetime import datetime\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": null,
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||
"metadata": {
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"id": "uuTX-xonNeOK"
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},
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"outputs": [],
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"source": [
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"# Constants\n",
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"\n",
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"BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
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"PROJECT_NAME = \"pricer\"\n",
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"HF_USER = \"muthash\"\n",
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"\n",
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"# Data\n",
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"\n",
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"DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
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"MAX_SEQUENCE_LENGTH = 182\n",
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"\n",
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"# Run name for saving the model in the hub\n",
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"RUN_NAME = f\"{datetime.now():%Y-%m-%d_%H.%M.%S}\"\n",
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"PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n",
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"HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
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"\n",
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"# Hyperparameters for QLoRA\n",
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"\n",
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"LORA_R = 32\n",
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"LORA_ALPHA = 64\n",
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"TARGET_MODULES = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"]\n",
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"LORA_DROPOUT = 0.1\n",
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"QUANT_4_BIT = True\n",
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"\n",
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"# Hyperparameters for Training\n",
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"\n",
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"EPOCHS = 1 # you can do more epochs if you wish, but only 1 is needed - more is probably overkill\n",
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"BATCH_SIZE = 16 # on an A100 box this can go up to 16\n",
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"GRADIENT_ACCUMULATION_STEPS = 1\n",
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"LEARNING_RATE = 3e-4\n",
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"LR_SCHEDULER_TYPE = 'cosine'\n",
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"WARMUP_RATIO = 0.06\n",
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"OPTIMIZER = \"paged_adamw_32bit\"\n",
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"\n",
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"# Admin config - note that SAVE_STEPS is how often it will upload to the hub\n",
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"# I've changed this from 5000 to 2000 so that you get more frequent saves\n",
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"\n",
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"STEPS = 50\n",
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"SAVE_STEPS = 2000\n",
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"LOG_TO_WANDB = True\n",
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"\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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||
"metadata": {
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"id": "WyFPZeMcM88v"
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},
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"outputs": [],
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"source": [
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"# Log in to HuggingFace\n",
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"\n",
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"hf_token = userdata.get('HF_TOKEN')\n",
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"login(hf_token, add_to_git_credential=True)\n",
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"\n",
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"# Log in to Weights & Biases\n",
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"wandb_api_key = userdata.get('WANDB_API_KEY')\n",
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"os.environ[\"WANDB_API_KEY\"] = wandb_api_key\n",
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"wandb.login()\n",
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"\n",
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"# Configure Weights & Biases to record against our project\n",
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"os.environ[\"WANDB_PROJECT\"] = PROJECT_NAME\n",
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"os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\" if LOG_TO_WANDB else \"end\"\n",
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"os.environ[\"WANDB_WATCH\"] = \"gradients\""
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": null,
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"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 177,
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||
"referenced_widgets": [
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]
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},
|
||
"id": "cvXVoJH8LS6u",
|
||
"outputId": "4c0d9ede-7650-440f-b1c3-30367405c56b"
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||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"dataset = load_dataset(DATASET_NAME)\n",
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"train = dataset['train']\n",
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"test = dataset['test']\n",
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"\n",
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"train = train.select(range(20000))"
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]
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},
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||
{
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||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 121
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||
},
|
||
"id": "8_SUsKqA23Gc",
|
||
"outputId": "90c72a1d-ac2e-4ce2-9772-2540a7eef3a7"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if LOG_TO_WANDB:\n",
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||
" wandb.init(project=PROJECT_NAME, name=RUN_NAME)"
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]
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||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "9lb7M9xn46wx"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# quantization\n",
|
||
"\n",
|
||
"if QUANT_4_BIT:\n",
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||
" quant_config = BitsAndBytesConfig(\n",
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||
" 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/",
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||
"height": 418,
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"referenced_widgets": [
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]
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},
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||
"id": "R_O04fKxMMT-",
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||
"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": [
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"f477da9bc5d1478db4897c8325ec8704",
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"edeaf1c7918e4ecd8f34fab2ddb0250f",
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]
|
||
},
|
||
"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": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 1000,
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"referenced_widgets": [
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]
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},
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"id": "GfvAxnXPvB7w",
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"outputId": "d61e240c-078d-4f0f-9e6f-4b3be9137d21"
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},
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"outputs": [],
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||
"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",
|
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"execution_count": null,
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||
"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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},
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"outputId": "c5c5a957-b212-4298-9355-e60cb812fabd"
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||
},
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"outputs": [],
|
||
"source": [
|
||
"if LOG_TO_WANDB:\n",
|
||
" wandb.finish()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"accelerator": "GPU",
|
||
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"gpuType": "A100",
|
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"provenance": []
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||
},
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"display_name": "Python 3",
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"name": "python3"
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},
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"name": "python"
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