fine-tuning-os-model-qlora
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Ransford Okpoti
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{
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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": "ogushXV4ZGMi"
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},
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"source": [
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"# Fine-Tuning an OpenSource Model using QLoRA"
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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": "chQmy4_HXhgr"
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},
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"outputs": [],
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"source": [
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"%pip install -qU peft trl bitsandbytes datasets 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": "zHZJoUeQZJNo"
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from transformers import (\n",
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" AutoModelForCausalLM,\n",
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" AutoTokenizer,\n",
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" BitsAndBytesConfig,\n",
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" TrainingArguments\n",
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")\n",
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"from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n",
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"from trl import SFTConfig, SFTTrainer\n",
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"from datasets import load_dataset"
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]
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},
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{
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"cell_type": "code",
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"source": [
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"MODEL_NAME = \"mistralai/Mistral-7B-v0.1\"\n",
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"\n",
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"\n",
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"bnb_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_use_double_quant=True,\n",
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" bnb_4bit_quant_type=\"nf4\",\n",
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" bnb_4bit_compute_dtype=torch.bfloat16\n",
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")"
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],
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"metadata": {
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"id": "NyDHu1vrZ1gO"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"model = AutoModelForCausalLM.from_pretrained(\n",
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" MODEL_NAME,\n",
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" quantization_config=bnb_config,\n",
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" device_map=\"auto\",\n",
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" trust_remote_code=True\n",
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")\n",
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"\n",
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"tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
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"tokenizer.pad_token = tokenizer.eos_token\n",
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"tokenizer.padding_side = \"right\"\n",
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"\n",
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"\n",
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"model = prepare_model_for_kbit_training(model)"
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],
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"metadata": {
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"id": "v2X2xM-dZ7fN"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"lora_config = LoraConfig(\n",
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" r=8,\n",
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" lora_alpha=16,\n",
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" target_modules=[\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"],\n",
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" lora_dropout=0.05,\n",
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" bias=\"none\",\n",
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" task_type=\"CAUSAL_LM\"\n",
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")\n",
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"\n",
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"model = get_peft_model(model, lora_config)\n",
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"model.print_trainable_parameters()"
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],
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"metadata": {
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"id": "akK6pJnLaeIr"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"DATA_DIR = '/content/data'\n",
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"\n",
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"data_files = [\n",
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" f'{DATA_DIR}/all_beauty_train.parquet',\n",
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"]\n",
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"\n",
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"dataset = load_dataset('parquet', data_files=data_files, split='train')\n",
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"\n",
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"train_test = dataset.train_test_split(train_size=100, test_size=20, seed=42)\n",
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"train_dataset = train_test[\"train\"]\n",
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"\n",
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"test_dataset = load_dataset('parquet', data_files=[f'{DATA_DIR}/all_beauty_test.parquet'], split='train')"
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],
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"metadata": {
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"id": "Nm1O_IjBbNa0"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"sft_config = SFTConfig(\n",
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" output_dir=\"./price-prediction-qlora\",\n",
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" num_train_epochs=1,\n",
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" per_device_train_batch_size=4,\n",
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" gradient_accumulation_steps=2,\n",
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" gradient_checkpointing=True,\n",
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" optim=\"paged_adamw_8bit\",\n",
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" learning_rate=2e-4,\n",
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" lr_scheduler_type=\"cosine\",\n",
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" warmup_steps=50,\n",
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" logging_steps=10,\n",
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" save_strategy=\"no\",\n",
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" fp16=False,\n",
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" bf16=True,\n",
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" max_grad_norm=0.3,\n",
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" save_total_limit=2,\n",
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" group_by_length=True,\n",
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" report_to=\"none\",\n",
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" packing=False,\n",
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" dataset_text_field=\"text\",\n",
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")\n",
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"\n",
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"\n",
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"trainer = SFTTrainer(\n",
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" model=model,\n",
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" args=sft_config,\n",
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" train_dataset=train_dataset,\n",
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")\n",
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"\n"
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],
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"metadata": {
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"id": "a8Nx8GJTb-Wz"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"### Start Training"
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],
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"metadata": {
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"id": "UmH5E6Xvn8so"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"print(\"Starting training...\")\n",
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"\n",
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"trainer.train()\n",
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"\n",
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"trainer.model.save_pretrained(\"./price-prediction-final\")\n",
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"tokenizer.save_pretrained(\"./price-prediction-final\")\n",
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"\n",
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"print(\"Training complete! LoRA adapters saved to ./price-prediction-final\")"
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],
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"metadata": {
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"id": "-nXZ5O_ifFVh"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
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"\n",
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"def predict_price_inmemory(prompt, model, tokenizer):\n",
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"\n",
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" model.eval()\n",
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"\n",
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" inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
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"\n",
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" with torch.no_grad():\n",
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" with torch.amp.autocast(device_type='cuda', dtype=torch.bfloat16):\n",
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" outputs = model.generate(\n",
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" **inputs,\n",
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" max_new_tokens=10,\n",
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" temperature=0.1,\n",
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" do_sample=False,\n",
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" pad_token_id=tokenizer.eos_token_id\n",
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" )\n",
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"\n",
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" result = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
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"\n",
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" if \"Price is $\" in result:\n",
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" predicted = result.split(\"Price is $\")[-1].strip()\n",
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"\n",
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" import re\n",
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" match = re.search(r'(\\d+\\.?\\d*)', predicted)\n",
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" if match:\n",
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" return match.group(1)\n",
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" return predicted"
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],
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"metadata": {
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"id": "LSvPhf-3fYaZ"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"### Eye-test Validation\n",
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"\n",
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"Not the best I know, but I wanted to go through the entire process myself and not enough time on my hands."
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],
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"metadata": {
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"id": "m6L5CET_sXmx"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"for item in test_dataset.take(5):\n",
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" prompt = item[\"text\"]\n",
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" actual_price = item[\"price\"]\n",
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"\n",
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" predicted_price = float(predict_price_inmemory(prompt, model, tokenizer))\n",
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" print(\"\\n\" + \"*\" * 80)\n",
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" print(prompt)\n",
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"\n",
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" print(f\"Prediction: ${predicted_price}. Actual: ${actual_price}. Diff {abs(predicted_price - actual_price):,.2f}\")\n"
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],
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"metadata": {
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"id": "6pSYhLn_kROQ"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Loading Somewhere in the future\n",
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"\n",
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"It can even be loaded in a different notebook."
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],
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"metadata": {
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"id": "OnJAD7YihyAD"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
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"from peft import PeftModel\n",
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"import torch\n",
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"\n",
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"bnb_config = BitsAndBytesConfig(\n",
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" load_in_4bit=True,\n",
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" bnb_4bit_use_double_quant=True,\n",
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" bnb_4bit_quant_type=\"nf4\",\n",
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" bnb_4bit_compute_dtype=torch.bfloat16\n",
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")\n",
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"\n",
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"base_model = AutoModelForCausalLM.from_pretrained(\n",
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" MODEL_NAME,\n",
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" quantization_config=bnb_config,\n",
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" device_map=\"auto\",\n",
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" trust_remote_code=True\n",
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")\n",
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"\n",
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"\n",
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"model = PeftModel.from_pretrained(base_model, \"./price-prediction-final\")\n",
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"tokenizer = AutoTokenizer.from_pretrained(\"./price-prediction-final\")"
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],
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"metadata": {
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"id": "5RCClHQHijes"
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},
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "T4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"name": "python3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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