fine-tune-llama3-using-qlora.ipynb

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    "# Introduction\n",
    "\n",
    "\n",
    "In this notebook we demostrate how to finetune **Llama3** (8B) model from Meta using **QLoRA** and **SFTTrainer** from **tlr**.\n",
    "\n",
    "## What is Llama3?\n",
    "\n",
    "Llama3 is the latest release of open-source LLMs from Meta, with features pretrained and instruction-fine-tuned language models with 8B and 70B parameters.\n",
    "\n",
    "## What is LoRA?\n",
    "\n",
    "LoRA stands for Low-Rank Adaptation. It is a method used to fine-tune large language models (LLMs) by freezing the weights of the LLM and injecting trainable rank-decomposition matrices. The number of trainable parameters during fine-tunning will decrease therefore considerably. According to LoRA paper, this number decreases 10,000 times, and the computational resources size decreases 3 times.\n",
    "\n",
    "\n",
    "## What is QLoRA?\n",
    "\n",
    "QLoRA builds on LoRA by incorporating quantization techniques to further reduce memory usage while maintaining, or even enhancing, model performance. With QLoRA it is possible to finetune a 70B parameter model that requires 36 GPUs with only 2!\n",
    "\n",
    "\n",
    "## What is PEFT?\n",
    "\n",
    "Parameter-efficient Fine-tuning (PEFT) is a technique used in Natural Language Processing (NLP) to improve the performance of pre-trained language models on specific downstream tasks. It involves reusing the pre-trained model’s parameters and fine-tuning them on a smaller dataset, which saves computational resources and time compared to training the entire model from scratch. PEFT achieves this efficiency by freezing some of the layers of the pre-trained model and only fine-tuning the last few layers that are specific to the downstream task.\n",
    "\n",
    "## What is SFTTrainer?\n",
    "\n",
    "SFT in SFTTrainer stands for supervised fine-tuning. The **trl** (Transformer Reinforcement Learning) library from HuggingFace provides a simple API to fine-tune models using SFTTrainer.\n",
    "\n",
    "## What is UltraChat200k?  \n",
    "\n",
    "UltraChat-200k is an invaluable resource for natural language understanding, generation and dialog system research. With 1.4 million dialogues spanning a variety of topics, this parquet-formatted dataset offers researchers four distinct formats to aid in their studies: test_sft, train_sft, train_gen and test_gen. More details [here](https://www.kaggle.com/datasets/thedevastator/ultrachat-200k-nlp-dataset).\n",
    "\n",
    "## Inspiration\n",
    "\n",
    "For this notebook, I took inspiration from several sources:\n",
    "* [Efficiently fine-tune Llama 3 with PyTorch FSDP and Q-Lora](https://www.philschmid.de/fsdp-qlora-llama3)  \n",
    "* [Fine-tuning LLMs using LoRA](https://medium.com/@rajatsharma_33357/fine-tuning-llama-using-lora-fb3f48a557d5)  \n",
    "* [Fine-tuning Llama-3–8B-Instruct QLORA using low cost resources](https://medium.com/@avishekpaul31/fine-tuning-llama-3-8b-instruct-qlora-using-low-cost-resources-89075e0dfa04)  \n",
    "* [Llama2 Fine-Tuning with Low-Rank Adaptations (LoRA) on Gaudi 2 Processors](https://eduand-alvarez.medium.com/llama2-fine-tuning-with-low-rank-adaptations-lora-on-gaudi-2-processors-52cf1ee6ce11)  "
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   },
   "source": [
    "# Install and import libraries"
   ]
  },
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     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "keras-cv 0.8.2 requires keras-core, which is not installed.\r\n",
      "keras-nlp 0.9.3 requires keras-core, which is not installed.\r\n",
      "tensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\r\n",
      "apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.8 which is incompatible.\r\n",
      "apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\r\n",
      "apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 15.0.2 which is incompatible.\r\n",
      "beatrix-jupyterlab 2023.128.151533 requires jupyterlab~=3.6.0, but you have jupyterlab 4.1.6 which is incompatible.\r\n",
      "google-cloud-aiplatform 0.6.0a1 requires google-api-core[grpc]<2.0.0dev,>=1.22.2, but you have google-api-core 2.11.1 which is incompatible.\r\n",
      "google-cloud-automl 1.0.1 requires google-api-core[grpc]<2.0.0dev,>=1.14.0, but you have google-api-core 2.11.1 which is incompatible.\r\n",
      "google-cloud-pubsub 2.19.0 requires grpcio<2.0dev,>=1.51.3, but you have grpcio 1.51.1 which is incompatible.\r\n",
      "kfp 2.5.0 requires google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0 which is incompatible.\r\n",
      "libpysal 4.9.2 requires packaging>=22, but you have packaging 21.3 which is incompatible.\r\n",
      "libpysal 4.9.2 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "momepy 0.7.0 requires shapely>=2, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "osmnx 1.9.2 requires shapely>=2.0, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "spopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\r\n",
      "tensorflow 2.15.0 requires keras<2.16,>=2.15.0, but you have keras 3.2.1 which is incompatible.\r\n",
      "ydata-profiling 4.6.4 requires numpy<1.26,>=1.16.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "kfp 2.5.0 requires google-cloud-storage<3,>=2.2.1, but you have google-cloud-storage 1.44.0 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -q -U bitsandbytes\n",
    "!pip install -q -U transformers\n",
    "!pip install -q -U peft\n",
    "!pip install -q -U accelerate\n",
    "!pip install -q -U datasets\n",
    "!pip install -q -U trl"
   ]
  },
  {
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   "source": [
    "import pandas as pd\n",
    "from torch.utils.data import Dataset\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0ecb4a6d",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\r\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\r\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\r\n"
     ]
    }
   ],
   "source": [
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "wandb_key = user_secrets.get_secret(\"wandb_api\")\n",
    "import wandb\n",
    "! wandb login $wandb_key"
   ]
  },
  {
   "cell_type": "code",
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   "id": "4ec22411",
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-09-01 18:12:00.862481: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-09-01 18:12:00.862626: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-09-01 18:12:01.035721: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import torch\n",
    "from time import time\n",
    "from datasets import load_dataset\n",
    "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training\n",
    "from transformers import (\n",
    "    AutoConfig,\n",
    "    AutoModelForCausalLM,\n",
    "    AutoTokenizer,\n",
    "    BitsAndBytesConfig,\n",
    "    AutoTokenizer,\n",
    "    TrainingArguments,\n",
    ")\n",
    "from trl import SFTTrainer,setup_chat_format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97810f8f",
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     "start_time": "2024-09-01T18:12:11.645025",
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    },
    "tags": []
   },
   "source": [
    "# Initialize model\n",
    "\n",
    "\n",
    "The model used is:\n",
    "\n",
    "* **Model**: Llama3  \n",
    "* **Framework**: Transformers   \n",
    "* **Size**: 8B   \n",
    "* **Type**: 8b-chat-hf (hf stands for HuggingFace). \n",
    "* **Version**: V1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37c64df1",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "model_id = \"/kaggle/input/llama-3/transformers/8b-chat-hf/1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65ae0899",
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    "tags": []
   },
   "source": [
    "We are using quantization (with BitsAndBytes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eeae2dac",
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   "outputs": [],
   "source": [
    "compute_dtype = torch.bfloat16\n",
    "bnb_config = BitsAndBytesConfig(\n",
    "        load_in_4bit=True,\n",
    "        bnb_4bit_quant_type=\"nf4\",\n",
    "        bnb_4bit_compute_dtype=compute_dtype,\n",
    "        bnb_4bit_use_double_quant=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7ff66c3",
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   "source": [
    "We define the model configuration, the model (using AutoModelForCausalLM) and the tokenizer (using AutoTokenizer)."
   ]
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   "outputs": [
    {
     "data": {
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       "model_id": "cda72edfdb6a48039140cb88efe3eae7",
       "version_major": 2,
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      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "Prepare model, tokenizer: 113.211 sec.\n"
     ]
    }
   ],
   "source": [
    "time_start = time()\n",
    "\n",
    "model_config = AutoConfig.from_pretrained(\n",
    "    model_id,\n",
    "    trust_remote_code=True,\n",
    "    max_new_tokens=1024\n",
    ")\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    trust_remote_code=True,\n",
    "    config=model_config,\n",
    "    quantization_config=bnb_config,\n",
    "    device_map='auto',\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "time_end = time()\n",
    "print(f\"Prepare model, tokenizer: {round(time_end-time_start, 3)} sec.\")"
   ]
  },
  {
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   "execution_count": 8,
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    "tags": []
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   "outputs": [],
   "source": [
    "model, tokenizer = setup_chat_format(model, tokenizer)\n",
    "model = prepare_model_for_kbit_training(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "841a48fe",
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   },
   "source": [
    "# Prepare the dataset   \n",
    "\n",
    "\n",
    "We will use 10K rows from the `ultrachat_200k` database."
   ]
  },
  {
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     "data": {
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       "model_id": "1036c001709b493e83c53fe344ca78fe",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "eea5c5d6639c493b88034cfe0fdb775f",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "e7f741350ede460388dd5a1e976402fb",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3faa8ed6838b4696bd1f8d3f2c610d3f",
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c6e236bff70f49918d0561c28379eb8f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train_sft split:   0%|          | 0/207865 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "78a18cdae74d42539ef4484c8b8f5487",
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "e4ee8df394cc4aad91fd574fa47c1682",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train_gen split:   0%|          | 0/256032 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d7f19df041b49109bbc6772b61a38b5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test_gen split:   0%|          | 0/28304 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0e80adbc68b435695d4e25c887e924b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=4):   0%|          | 0/10000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_name = \"HuggingFaceH4/ultrachat_200k\"\n",
    "dataset = load_dataset(dataset_name, split=\"train_sft\")\n",
    "dataset = dataset.shuffle(seed=42).select(range(10000))\n",
    "\n",
    "def format_chat_template(row):\n",
    "    chat = tokenizer.apply_chat_template(row[\"messages\"], tokenize=False)\n",
    "    return {\"text\":chat}\n",
    "\n",
    "processed_dataset = dataset.map(\n",
    "    format_chat_template,\n",
    "    num_proc= os.cpu_count(),\n",
    ")\n",
    "\n",
    "dataset = processed_dataset.train_test_split(test_size=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25896322",
   "metadata": {
    "papermill": {
     "duration": 0.010456,
     "end_time": "2024-09-01T18:14:40.862549",
     "exception": false,
     "start_time": "2024-09-01T18:14:40.852093",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b276c2",
   "metadata": {
    "papermill": {
     "duration": 0.010056,
     "end_time": "2024-09-01T18:14:40.883352",
     "exception": false,
     "start_time": "2024-09-01T18:14:40.873296",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training configuration   \n",
    "\n",
    "We set the rank for LoRA to 4, to reduce the number of trainable parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60c28393",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-01T18:14:40.907407Z",
     "iopub.status.busy": "2024-09-01T18:14:40.906417Z",
     "iopub.status.idle": "2024-09-01T18:14:40.912644Z",
     "shell.execute_reply": "2024-09-01T18:14:40.911713Z"
    },
    "papermill": {
     "duration": 0.020766,
     "end_time": "2024-09-01T18:14:40.914861",
     "exception": false,
     "start_time": "2024-09-01T18:14:40.894095",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "peft_config = LoraConfig(\n",
    "        lora_alpha=64,\n",
    "        lora_dropout=0.05,\n",
    "        r=4,\n",
    "        bias=\"none\",\n",
    "        task_type=\"CAUSAL_LM\",\n",
    "        target_modules= [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
    "                      \"gate_proj\", \"up_proj\", \"down_proj\",]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31298209",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-01T18:14:40.938463Z",
     "iopub.status.busy": "2024-09-01T18:14:40.937493Z",
     "iopub.status.idle": "2024-09-01T18:14:40.995046Z",
     "shell.execute_reply": "2024-09-01T18:14:40.993864Z"
    },
    "papermill": {
     "duration": 0.072158,
     "end_time": "2024-09-01T18:14:40.997260",
     "exception": false,
     "start_time": "2024-09-01T18:14:40.925102",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "training_arguments = TrainingArguments(\n",
    "        output_dir=\"./results_llama3_sft/\",\n",
    "        evaluation_strategy=\"steps\",\n",
    "        do_eval=True,\n",
    "        optim=\"paged_adamw_8bit\",\n",
    "        per_device_train_batch_size=8,\n",
    "        gradient_accumulation_steps=2,\n",
    "        per_device_eval_batch_size=8,\n",
    "        log_level=\"debug\",\n",
    "        save_steps=20,\n",
    "        logging_steps=1,\n",
    "        learning_rate=8e-6,\n",
    "        eval_steps=1,\n",
    "        max_steps=20,\n",
    "        num_train_epochs=20,\n",
    "        warmup_steps=3,\n",
    "        lr_scheduler_type=\"linear\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a23a3d9",
   "metadata": {
    "papermill": {
     "duration": 0.010403,
     "end_time": "2024-09-01T18:14:41.017153",
     "exception": false,
     "start_time": "2024-09-01T18:14:41.006750",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "97ccbe9d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-01T18:14:41.041129Z",
     "iopub.status.busy": "2024-09-01T18:14:41.040505Z",
     "iopub.status.idle": "2024-09-01T18:14:41.046116Z",
     "shell.execute_reply": "2024-09-01T18:14:41.045106Z"
    },
    "papermill": {
     "duration": 0.020457,
     "end_time": "2024-09-01T18:14:41.049086",
     "exception": false,
     "start_time": "2024-09-01T18:14:41.028629",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "os.environ[\"WANDB_DISABLED\"] = \"false\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "56b1b068",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-01T18:14:41.072720Z",
     "iopub.status.busy": "2024-09-01T18:14:41.072310Z",
     "iopub.status.idle": "2024-09-01T20:05:31.104943Z",
     "shell.execute_reply": "2024-09-01T20:05:31.103892Z"
    },
    "papermill": {
     "duration": 6650.046314,
     "end_time": "2024-09-01T20:05:31.107410",
     "exception": false,
     "start_time": "2024-09-01T18:14:41.061096",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field, max_seq_length. Will not be supported from version '1.0.0'.\n",
      "\n",
      "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n",
      "  warnings.warn(message, FutureWarning)\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:283: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
      "  warnings.warn(\n",
      "/opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:321: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b34ca788b084834aead491f8e063958",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/9900 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b3d02a84d7d4230b7e2dc9b91965410",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/100 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You have loaded a model on multiple GPUs. `is_model_parallel` attribute will be force-set to `True` to avoid any unexpected behavior such as device placement mismatching.\n",
      "max_steps is given, it will override any value given in num_train_epochs\n",
      "Currently training with a batch size of: 8\n",
      "***** Running training *****\n",
      "  Num examples = 9,900\n",
      "  Num Epochs = 1\n",
      "  Instantaneous batch size per device = 8\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 2\n",
      "  Total optimization steps = 20\n",
      "  Number of trainable parameters = 10,485,760\n",
      "Automatic Weights & Biases logging enabled, to disable set os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mgpreda\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: wandb version 0.17.8 is available!  To upgrade, please run:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:  $ pip install wandb --upgrade\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.16.6\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20240901_181520-s0w9cteu\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33m./results_llama3_sft/\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/gpreda/huggingface\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/gpreda/huggingface/runs/s0w9cteu\u001b[0m\n",
      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n",
      "/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:429: UserWarning: torch.utils.checkpoint: please pass in use_reentrant=True or use_reentrant=False explicitly. The default value of use_reentrant will be updated to be False in the future. To maintain current behavior, pass use_reentrant=True. It is recommended that you use use_reentrant=False. Refer to docs for more details on the differences between the two variants.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [20/20 1:48:08, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.539700</td>\n",
       "      <td>1.800820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.512300</td>\n",
       "      <td>1.795524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2.053300</td>\n",
       "      <td>1.785397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.607400</td>\n",
       "      <td>1.771167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.608000</td>\n",
       "      <td>1.758731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.471000</td>\n",
       "      <td>1.747921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.724600</td>\n",
       "      <td>1.738365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.692400</td>\n",
       "      <td>1.729980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.984200</td>\n",
       "      <td>1.722534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>1.605100</td>\n",
       "      <td>1.715840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>1.531000</td>\n",
       "      <td>1.709881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>1.842400</td>\n",
       "      <td>1.704595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1.889600</td>\n",
       "      <td>1.700004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1.737900</td>\n",
       "      <td>1.696017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1.530600</td>\n",
       "      <td>1.692662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>1.607500</td>\n",
       "      <td>1.689872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>1.564100</td>\n",
       "      <td>1.687619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>1.685300</td>\n",
       "      <td>1.685935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>1.406600</td>\n",
       "      <td>1.684798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1.402500</td>\n",
       "      <td>1.684225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 100\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to ./results_llama3_sft/checkpoint-20\n",
      "loading configuration file /kaggle/input/llama-3/transformers/8b-chat-hf/1/config.json\n",
      "Model config LlamaConfig {\n",
      "  \"architectures\": [\n",
      "    \"LlamaForCausalLM\"\n",
      "  ],\n",
      "  \"attention_bias\": false,\n",
      "  \"attention_dropout\": 0.0,\n",
      "  \"bos_token_id\": 128000,\n",
      "  \"eos_token_id\": 128001,\n",
      "  \"hidden_act\": \"silu\",\n",
      "  \"hidden_size\": 4096,\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 14336,\n",
      "  \"max_position_embeddings\": 8192,\n",
      "  \"mlp_bias\": false,\n",
      "  \"model_type\": \"llama\",\n",
      "  \"num_attention_heads\": 32,\n",
      "  \"num_hidden_layers\": 32,\n",
      "  \"num_key_value_heads\": 8,\n",
      "  \"pretraining_tp\": 1,\n",
      "  \"rms_norm_eps\": 1e-05,\n",
      "  \"rope_scaling\": null,\n",
      "  \"rope_theta\": 500000.0,\n",
      "  \"tie_word_embeddings\": false,\n",
      "  \"torch_dtype\": \"bfloat16\",\n",
      "  \"transformers_version\": \"4.44.2\",\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 128256\n",
      "}\n",
      "\n",
      "/opt/conda/lib/python3.10/site-packages/peft/utils/save_and_load.py:232: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n",
      "tokenizer config file saved in ./results_llama3_sft/checkpoint-20/tokenizer_config.json\n",
      "Special tokens file saved in ./results_llama3_sft/checkpoint-20/special_tokens_map.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=20, training_loss=1.6497756600379945, metrics={'train_runtime': 6611.3642, 'train_samples_per_second': 0.048, 'train_steps_per_second': 0.003, 'total_flos': 7387957124136960.0, 'train_loss': 1.6497756600379945, 'epoch': 0.03231017770597738})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "        model=model,\n",
    "        train_dataset=dataset['train'],\n",
    "        eval_dataset=dataset['test'],\n",
    "        peft_config=peft_config,\n",
    "        dataset_text_field=\"text\",\n",
    "        max_seq_length=512,\n",
    "        tokenizer=tokenizer,\n",
    "        args=training_arguments,\n",
    ")\n",
    "\n",
    "trainer.train()"
   ]
  },
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     "exception": false,
     "start_time": "2024-09-01T20:05:31.141412",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Conclusions\n",
    "\n",
    "By reducing the batch_size, max_seq_length and the LoRA rank, we managed to run the Llama3 fine-tunning with QLoRA in the Kaggle environment.\n",
    "\n",
    "**Note**: we set `os.environ[\"WANDB_DISABLED\"]` to `True` and `save_steps` to `20`. We intend to save the last checkpoint as a **Kaggle Model**.\n"
   ]
  }
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