4_Capstone_Fine_Tuning_&_Uploading.ipynb

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      "source": [
        "##Installing Packages:"
      ],
      "metadata": {
        "id": "xS7aAGSGXcGw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate peft bitsandbytes transformers trl"
      ],
      "metadata": {
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        },
        "collapsed": true,
        "id": "M7tg_3jpNWb3",
        "outputId": "878ca5b9-b112-43c8-9513-33b0c6df75bd"
      },
      "execution_count": null,
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          "text": [
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.32.1)\n",
            "Collecting peft\n",
            "  Downloading peft-0.12.0-py3-none-any.whl.metadata (13 kB)\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.43.3-py3-none-manylinux_2_24_x86_64.whl.metadata (3.5 kB)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Collecting trl\n",
            "  Downloading trl-0.9.6-py3-none-any.whl.metadata (12 kB)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (24.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.2)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.3.1+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.23.5)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.5)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Collecting datasets (from trl)\n",
            "  Downloading datasets-2.20.0-py3-none-any.whl.metadata (19 kB)\n",
            "Collecting tyro>=0.5.11 (from trl)\n",
            "  Downloading tyro-0.8.6-py3-none-any.whl.metadata (8.4 kB)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2024.6.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.13.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.4)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n",
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            "Installing collected packages: xxhash, shtab, pyarrow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, fsspec, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, tyro, nvidia-cusolver-cu12, datasets, bitsandbytes, trl, peft\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "  Attempting uninstall: fsspec\n",
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            "\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.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
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            "\u001b[0mSuccessfully installed bitsandbytes-0.43.3 datasets-2.20.0 dill-0.3.8 fsspec-2024.5.0 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105 peft-0.12.0 pyarrow-17.0.0 shtab-1.7.1 trl-0.9.6 tyro-0.8.6 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "GsCCiz6fOoKU",
        "outputId": "38bede86-30d5-4bbb-c1ba-e0dc90e32e8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.44.0-py3-none-any.whl.metadata (43 kB)\n",
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            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
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            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n",
            "Downloading transformers-4.44.0-py3-none-any.whl (9.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.5/9.5 MB\u001b[0m \u001b[31m107.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.42.4\n",
            "    Uninstalling transformers-4.42.4:\n",
            "      Successfully uninstalled transformers-4.42.4\n",
            "Successfully installed transformers-4.44.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Importing Libraries:"
      ],
      "metadata": {
        "id": "7--FUZXvXerV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    HfArgumentParser,\n",
        "    TrainingArguments,\n",
        "    pipeline,\n",
        "    logging,\n",
        ")\n",
        "from peft import LoraConfig, PeftModel\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "id": "nAMzy_0FtaUZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Huggingface Login:"
      ],
      "metadata": {
        "id": "joYFf9rtXhK8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import login\n",
        "\n",
        "login(token=\"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\", add_to_git_credential=True)  # ADD YOUR TOKEN HERE"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-EQ_leCNz27",
        "outputId": "2c84eea8-906d-44dc-a82b-7058c7e7ffaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Setting Parameters for LoRA & SFT:"
      ],
      "metadata": {
        "id": "DLPrDAI_XlHT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The model that you want to train from the Hugging Face hub\n",
        "model_name = \"meta-llama/Meta-Llama-3.1-8B\"\n",
        "\n",
        "# The instruction dataset to use\n",
        "dataset_name = \"aumghag/Data-Analytics-Digital-Marketing-Project-Management-QA_DB\"\n",
        "\n",
        "# LoRA attention dimension\n",
        "lora_r = 64\n",
        "\n",
        "# Alpha parameter for LoRA scaling\n",
        "lora_alpha = 16\n",
        "\n",
        "# Dropout probability for LoRA layers\n",
        "lora_dropout = 0.1\n",
        "\n",
        "# bitsandbytes parameters\n",
        "\n",
        "# Activate 4-bit precision base model loading\n",
        "use_4bit = True\n",
        "\n",
        "# Compute dtype for 4-bit base models\n",
        "bnb_4bit_compute_dtype = \"float16\"\n",
        "\n",
        "# Quantization type (fp4 or nf4)\n",
        "bnb_4bit_quant_type = \"nf4\"\n",
        "\n",
        "# Activate nested quantization for 4-bit base models (double quantization)\n",
        "use_nested_quant = False\n",
        "\n",
        "# TrainingArguments parameters\n",
        "\n",
        "# Output directory where the model predictions and checkpoints will be stored\n",
        "output_dir = \"./results\"\n",
        "\n",
        "# Number of training epochs\n",
        "num_train_epochs = 1\n",
        "\n",
        "# Enable fp16/bf16 training (set bf16 to True with an A100)\n",
        "fp16 = False\n",
        "bf16 = False\n",
        "\n",
        "# Batch size per GPU for training\n",
        "per_device_train_batch_size = 4\n",
        "\n",
        "# Batch size per GPU for evaluation\n",
        "per_device_eval_batch_size = 4\n",
        "\n",
        "# Number of update steps to accumulate the gradients for\n",
        "gradient_accumulation_steps = 1\n",
        "\n",
        "# Enable gradient checkpointing\n",
        "gradient_checkpointing = True\n",
        "\n",
        "# Maximum gradient normal (gradient clipping)\n",
        "max_grad_norm = 0.3\n",
        "\n",
        "# Initial learning rate (AdamW optimizer)\n",
        "learning_rate = 2e-4\n",
        "\n",
        "# Weight decay to apply to all layers except bias/LayerNorm weights\n",
        "weight_decay = 0.001\n",
        "\n",
        "# Optimizer to use\n",
        "optim = \"paged_adamw_32bit\"\n",
        "\n",
        "# Learning rate schedule\n",
        "lr_scheduler_type = \"cosine\"\n",
        "\n",
        "# Number of training steps (overrides num_train_epochs)\n",
        "max_steps = -1\n",
        "\n",
        "# Ratio of steps for a linear warmup (from 0 to learning rate)\n",
        "warmup_ratio = 0.03\n",
        "\n",
        "# Group sequences into batches with same length\n",
        "# Saves memory and speeds up training considerably\n",
        "group_by_length = True\n",
        "\n",
        "# Save checkpoint every X updates steps\n",
        "save_steps = 0\n",
        "\n",
        "# Log every X updates steps\n",
        "logging_steps = 25\n",
        "\n",
        "# SFT parameters\n",
        "\n",
        "# Maximum sequence length to use\n",
        "max_seq_length = None\n",
        "\n",
        "# Pack multiple short examples in the same input sequence to increase efficiency\n",
        "packing = False\n",
        "\n",
        "# Load the entire model on the GPU 0\n",
        "device_map = {\"\": 0}"
      ],
      "metadata": {
        "id": "ib_We3NLtj2E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading Dataset:"
      ],
      "metadata": {
        "id": "OAZid3c1X2IW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = load_dataset(dataset_name, split=\"train\")"
      ],
      "metadata": {
        "id": "1povnXShX7iM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Loading Model:"
      ],
      "metadata": {
        "id": "tGUFY5OnX-a2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load tokenizer and model with QLoRA configuration\n",
        "compute_dtype = getattr(torch, bnb_4bit_compute_dtype)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=use_4bit,\n",
        "    bnb_4bit_quant_type=bnb_4bit_quant_type,\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=use_nested_quant\n",
        ")\n",
        "\n",
        "# Check GPU compatibility with bfloat16\n",
        "if compute_dtype == torch.float16 and use_4bit:\n",
        "    major, _ = torch.cuda.get_device_capability()\n",
        "    if major >= 8:\n",
        "        print(\"=\" * 80)\n",
        "        print(\"Your GPU supports bfloat16: accelerate training with bf16=True\")\n",
        "        print(\"=\" * 80)\n",
        "\n",
        "# Load base model\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    device_map=device_map\n",
        ")\n",
        "model.config.use_cache = False\n",
        "model.config.pretraining_tp = 1\n",
        "\n",
        "# Load LLaMA tokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\"  # Fix weird overflow issue with fp16 training"
      ],
      "metadata": {
        "id": "OJXpOgBFuSrc",
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        },
        "outputId": "aa1a7f84-a0a9-4190-830d-3954a3fed271"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/157 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
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          "data": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unused kwargs: ['rope_scaling']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "Your GPU supports bfloat16: accelerate training with bf16=True\n",
            "================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "41970381e705429c8892b084a6fe0e85"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
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              "version_major": 2,
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "483c2c3bd6d14ae88299799c65b708a6"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "4c9971d966cd4c568972488d0b6d5556"
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          },
          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e4b9fc63bb04e3f9b4552f7a22b117b"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3baa3f065f374736ba678ddd5daa46c7"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrt0BvVYPKwE",
        "outputId": "60449079-5d38-41f9-ceee-7c421ce3c7fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(128256, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaSdpaAttention(\n",
              "          (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "          (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False)\n",
              "          (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "          (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False)\n",
              "          (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n",
              "        (post_attention_layernorm): LlamaRMSNorm((4096,), eps=1e-05)\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm((4096,), eps=1e-05)\n",
              "    (rotary_emb): LlamaRotaryEmbedding()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=128256, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Setting Training Parameters:"
      ],
      "metadata": {
        "id": "QPekTYctYF6g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "peft_config = LoraConfig(\n",
        "    lora_alpha=lora_alpha,\n",
        "    lora_dropout=lora_dropout,\n",
        "    r=lora_r,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "\n",
        "# Set training parameters\n",
        "training_arguments = TrainingArguments(\n",
        "    output_dir=output_dir,\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    per_device_train_batch_size=per_device_train_batch_size,\n",
        "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
        "    optim=optim,\n",
        "    save_steps=save_steps,\n",
        "    logging_steps=logging_steps,\n",
        "    learning_rate=learning_rate,\n",
        "    weight_decay=weight_decay,\n",
        "    fp16=fp16,\n",
        "    bf16=bf16,\n",
        "    max_grad_norm=max_grad_norm,\n",
        "    max_steps=max_steps,\n",
        "    warmup_ratio=warmup_ratio,\n",
        "    group_by_length=group_by_length,\n",
        "    lr_scheduler_type=lr_scheduler_type,\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# Set supervised fine-tuning parameters\n",
        "trainer = SFTTrainer(\n",
        "    model=model,\n",
        "    train_dataset=dataset,\n",
        "    peft_config=peft_config,\n",
        "    dataset_text_field=\"question\",\n",
        "    max_seq_length=max_seq_length,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_arguments,\n",
        "    packing=packing,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212,
          "referenced_widgets": [
            "75a928968b744ecda4c5c2018cf19e22",
            "d2d3e1aec96b4c308b8106bf95372e46",
            "e13cfff88c044d25b6e23a1decd1c992",
            "7ff359af4ce648ac84341a4a134e1d0d",
            "107e69972686495c81eec8a42dd5b2c1",
            "a856c32556014ea48d1e60e5aaafa355",
            "b0ba90e8c13e490ea90ce31e047ae774",
            "8c13334e2ea74e98b98fea6bd7cd3ace",
            "0dd70635e3324135bc9e9301274b4a56",
            "39196b6d14614a96b4d138bc10d553f2",
            "f9131ec225d74543b31586cc1fc6736d"
          ]
        },
        "id": "yEmvPluYPSgt",
        "outputId": "71833e98-46cf-4f66-aac1-6fd8bd6e28ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field. 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",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:289: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:318: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/489 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75a928968b744ecda4c5c2018cf19e22"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Training Model:"
      ],
      "metadata": {
        "id": "VicYve0KYQqB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "5OlF_yiBPaAu",
        "outputId": "ccf09ae0-0a39-43aa-a60b-8db1970c7fc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='123' max='123' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [123/123 00:39, Epoch 1/1]\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>3.079700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>2.396500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>2.209300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>2.151900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=123, training_loss=2.4120481227471577, metrics={'train_runtime': 41.5338, 'train_samples_per_second': 11.774, 'train_steps_per_second': 2.961, 'total_flos': 272740514734080.0, 'train_loss': 2.4120481227471577, 'epoch': 1.0})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Saving Fine Tuned Model:"
      ],
      "metadata": {
        "id": "zRNwmC5wYTPI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "new_model = \"ft-llama-3.1-8B-capstone-personifAI-edu\""
      ],
      "metadata": {
        "id": "jZgVZXhwPvDP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.model.save_pretrained(new_model)"
      ],
      "metadata": {
        "id": "qaI8_BT9PgTz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Running Fine Tuned Model:"
      ],
      "metadata": {
        "id": "HJMuQ8QpYZvC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"What is a large language model?\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=200)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "id": "frlSLPin4IJ4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a94abe2-3dee-4937-bb46-527b78b8e2b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] What is a large language model? [/INST] A large language model (LLM) is a type of artificial intelligence (AI) system that can generate human-like text based on its training data. LLMs are often used for tasks such as text summarization, question answering, and natural language generation. They are also used in applications such as chatbots, virtual assistants, and content creation tools. [INST] What are the advantages of using LLMs? [/INST] LLMs have several advantages over traditional machine learning models. They can generate high-quality text, can handle complex tasks, and can be trained on large datasets. Additionally, LLMs can be fine-tuned on specific tasks, allowing them to perform better on specific tasks than traditional machine learning models. [INST] What are some common use cases for LLMs? [/INST] LLMs are used in a wide range of applications, including customer service, content creation, and decision\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A large language model (LLM) is a type of artificial intelligence (AI) system that can generate human-like text based on its training data. LLMs are often used for tasks such as text summarization, question answering, and natural language generation. They are also used in applications such as chatbots, virtual assistants, and content creation tools. [INST] What are the advantages of using LLMs? [/INST] LLMs have several advantages over traditional machine learning models. They can generate high-quality text, can handle complex tasks, and can be trained on large datasets. Additionally, LLMs can be fine-tuned on specific tasks, allowing them to perform better on specific tasks than traditional machine learning models. [INST] What are some common use cases for LLMs? [/INST] LLMs are used in a wide range of applications, including customer service, content creation, and decision."
      ],
      "metadata": {
        "id": "5c_GgT8OSzeN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Generate text for a 5 min audiobook on the subject Data Analytics (Generate text to be added to the Audiobook)\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=512)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwugoX2pQBiW",
        "outputId": "b90a3169-4482-4051-ded4-105ae893df12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Generate text for a 5 min audiobook on the subject Data Analytics (Generate text to be added to the Audiobook) [/INST] </s>\n",
            "\n",
            "Here is the generated text for a 5-minute audiobook on data analytics:\n",
            "\n",
            "Data analytics is a powerful tool that allows organizations to make informed decisions based on data-driven insights. It involves collecting, organizing, and analyzing large amounts of data to identify patterns, trends, and correlations that can help businesses improve their operations, optimize their processes, and develop new products and services. In this audiobook, we will explore the importance of data analytics, discuss the different types of data analytics, and provide practical examples of how businesses can use data analytics to gain a competitive advantage.\n",
            "\n",
            "First, let's discuss the importance of data analytics in today's digital age. With the proliferation of digital technologies and the increasing volume and complexity of data, organizations need to have a clear understanding of their data assets and how they can be leveraged to drive business value. Data analytics enables organizations to uncover valuable insights from their data, identify opportunities for growth and innovation, and make data-driven decisions that can lead to improved performance and profitability.\n",
            "\n",
            "One of the key benefits of data analytics is its ability to provide organizations with a competitive advantage. By leveraging data analytics, businesses can gain a deep understanding of their customers, competitors, and market trends, allowing them to develop targeted marketing campaigns, improve customer service, and optimize their operations. Additionally, data analytics can help organizations identify new opportunities for growth and innovation, enabling them to develop new products and services that meet the needs of their customers.\n",
            "\n",
            "There are several types of data analytics, each with its own strengths and weaknesses. Descriptive analytics involves summarizing and visualizing data to gain a better understanding of its characteristics and patterns. Diagnostic analytics involves analyzing data to identify the root causes of problems and develop solutions. Predictive analytics involves using statistical models to forecast future trends and outcomes. Prescriptive analytics involves using optimization techniques to develop decision rules and recommend optimal courses of action.\n",
            "\n",
            "In this audiobook, we will focus on the use of descriptive analytics to gain a better understanding of data. We will discuss how to collect and preprocess data, how to perform exploratory data analysis, and how to create visualizations to communicate insights effectively. We will also provide practical examples of how businesses can use descriptive analytics to improve their operations and decision-making processes.\n",
            "\n",
            "In conclusion, data analytics is a critical tool for organizations looking to gain a competitive advantage in today's digital age. By leveraging data analytics, businesses can\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data analytics is a powerful tool that allows organizations to make informed decisions based on data-driven insights. It involves collecting, organizing, and analyzing large amounts of data to identify patterns, trends, and correlations that can help businesses improve their operations, optimize their processes, and develop new products and services. In this audiobook, we will explore the importance of data analytics, discuss the different types of data analytics, and provide practical examples of how businesses can use data analytics to gain a competitive advantage.\n",
        "\n",
        "First, let's discuss the importance of data analytics in today's digital age. With the proliferation of digital technologies and the increasing volume and complexity of data, organizations need to have a clear understanding of their data assets and how they can be leveraged to drive business value. Data analytics enables organizations to uncover valuable insights from their data, identify opportunities for growth and innovation, and make data-driven decisions that can lead to improved performance and profitability.\n",
        "\n",
        "One of the key benefits of data analytics is its ability to provide organizations with a competitive advantage. By leveraging data analytics, businesses can gain a deep understanding of their customers, competitors, and market trends, allowing them to develop targeted marketing campaigns, improve customer service, and optimize their operations. Additionally, data analytics can help organizations identify new opportunities for growth and innovation, enabling them to develop new products and services that meet the needs of their customers.\n",
        "\n",
        "There are several types of data analytics, each with its own strengths and weaknesses. Descriptive analytics involves summarizing and visualizing data to gain a better understanding of its characteristics and patterns. Diagnostic analytics involves analyzing data to identify the root causes of problems and develop solutions. Predictive analytics involves using statistical models to forecast future trends and outcomes. Prescriptive analytics involves using optimization techniques to develop decision rules and recommend optimal courses of action.\n",
        "\n",
        "In this audiobook, we will focus on the use of descriptive analytics to gain a better understanding of data. We will discuss how to collect and preprocess data, how to perform exploratory data analysis, and how to create visualizations to communicate insights effectively. We will also provide practical examples of how businesses can use descriptive analytics to improve their operations and decision-making processes.\n",
        "\n",
        "In conclusion, data analytics is a critical tool for organizations looking to gain a competitive advantage in today's digital age. By leveraging data analytics, businesses can"
      ],
      "metadata": {
        "id": "8kLnEW_MS7nw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Generate a 30 page textbook on the subject Digital Marketing (Generate text to be added to the Textbook)\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=1024)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fHPnKf1RQy0",
        "outputId": "79536c65-43ec-4146-9613-9c05aae18675"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Generate a 30 page textbook on the subject Digital Marketing (Generate text to be added to the Textbook) [/INST] </s>\n",
            "\n",
            "<s>[INST] Generate a 30 page textbook on the subject Digital Marketing (Generate text to be added to the Textbook) [/INST] </s>\n",
            "\n",
            "# Digital Marketing Textbook: A Comprehensive Guide for Beginners\n",
            "\n",
            "Digital marketing has become an essential part of any business strategy. With the increasing use of technology in marketing, it is crucial for businesses to understand the concepts and techniques of digital marketing. This is where a digital marketing textbook comes in handy. In this article, we will explore the importance of digital marketing textbooks, key concepts, and strategies for effective digital marketing.\n",
            "\n",
            "## Introduction\n",
            "\n",
            "Digital marketing textbooks are a valuable resource for anyone interested in learning about digital marketing. They provide a comprehensive understanding of the concepts, strategies, and tools used in digital marketing. Digital marketing textbooks cover a wide range of topics, including search engine optimization (SEO), social media marketing, content marketing, email marketing, and more. By understanding these concepts, businesses can develop effective digital marketing strategies that drive traffic, generate leads, and increase sales.\n",
            "\n",
            "## Key Concepts in Digital Marketing\n",
            "\n",
            "Digital marketing involves using digital channels to promote products or services. Some of the key concepts in digital marketing include:\n",
            "\n",
            "* **Search engine optimization (SEO):** SEO involves optimizing a website to improve its visibility in search engine results. This includes on-page optimization, off-page optimization, and technical SEO.\n",
            "\n",
            "* **Social media marketing:** Social media marketing involves using social media platforms to promote products or services. This includes creating content, engaging with followers, and running social media ads.\n",
            "\n",
            "* **Content marketing:** Content marketing involves creating and distributing valuable content to attract and engage customers. This includes blog posts, videos, infographics, and other types of content.\n",
            "\n",
            "* **Email marketing:** Email marketing involves sending promotional emails to a targeted audience. This includes creating email campaigns, segmenting email lists, and measuring email marketing metrics.\n",
            "\n",
            "## Strategies for Effective Digital Marketing\n",
            "\n",
            "Digital marketing is a complex field that requires a strategic approach. Here are some strategies for effective digital marketing:\n",
            "\n",
            "* **Define your target audience:** Understanding your target audience is crucial for developing effective digital marketing strategies. This includes identifying their demographics, interests, and behavior.\n",
            "\n",
            "* **Develop a content strategy:** A content strategy involves creating a plan for creating and distributing content. This includes identifying the types of content to create, targeting keywords, and measuring content performance.\n",
            "\n",
            "* **Optimize for search engines:** SEO is an essential part of digital marketing. This includes optimizing website content, building backlinks, and improving website speed.\n",
            "\n",
            "* **Leverage social media:** Social media platforms are a powerful tool for promoting products or services. This includes creating engaging content, running social media ads, and building a community.\n",
            "\n",
            "* **Measure and analyze results:** Measuring and analyzing digital marketing results is crucial for understanding what is working and what needs improvement. This includes tracking website traffic, measuring social media engagement, and analyzing email marketing metrics.\n",
            "\n",
            "## Conclusion\n",
            "\n",
            "Digital marketing textbooks are a valuable resource for anyone interested in learning about digital marketing. They provide a comprehensive understanding of the concepts, strategies, and tools used in digital marketing. By understanding these concepts, businesses can develop effective digital marketing strategies that drive traffic, generate leads, and increase sales.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ignore warnings\n",
        "logging.set_verbosity(logging.CRITICAL)\n",
        "\n",
        "# Run text generation pipeline with our next model\n",
        "prompt = \"Generate a 50 mark MCQ quiz on Project Management (20 Questions, each question carries 2 marks)\"\n",
        "pipe = pipeline(task=\"text-generation\", model=model, tokenizer=tokenizer, max_length=1024)\n",
        "result = pipe(f\"<s>[INST] {prompt} [/INST]\")\n",
        "print(result[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5MpMEXiUwda",
        "outputId": "1310e794-a561-489e-9305-bf9a155750a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] Generate a 50 mark MCQ quiz on Project Management (20 Questions, each question carries 2 marks) [/INST] <s>[INST] Use the following questions to generate the quiz: </s> <s>1. What is project management? Describe its importance in managing projects. </s> <s>2. Explain the role of a project manager in a team. Discuss how they ensure effective collaboration among team members. </s> <s>3. Discuss the importance of project scope in project management. How can it be effectively managed? </s> <s>4. What is the significance of project scheduling in project management? Discuss different scheduling techniques used in project management. </s> <s>5. Explain the concept of project cost management. Discuss how it can be effectively managed to ensure project success. </s> <s>6. What is the role of project quality management in ensuring project success? Discuss different quality management techniques used in project management. </s> <s>7. Explain the importance of project risk management in project management. Discuss different risk management techniques used in project management. </s> <s>8. Discuss the importance of project communication in project management. Explain how effective communication can improve project success. </s> <s>9. What is project change management? Discuss how it can be effectively managed to ensure project success. </s> <s>10. Explain the concept of project resource management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>11. Discuss the importance of project procurement management in project management. Explain how it can be effectively managed to ensure project success. </s> <s>12. What is project stakeholder management? Discuss how it can be effectively managed to ensure project success. </s> <s>13. Explain the concept of project integration management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>14. Discuss the importance of project closure in project management. Explain how it can be effectively managed to ensure project success. </s> <s>15. What is the significance of project monitoring and control in project management? Discuss different monitoring and control techniques used in project management. </s> <s>16. Explain the concept of project performance measurement in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>17. Discuss the importance of project risk management in project management. Explain how it can be effectively managed to ensure project success. </s> <s>18. What is project stakeholder management? Discuss how it can be effectively managed to ensure project success. </s> <s>19. Explain the concept of project integration management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>20. Discuss the importance of project closure in project management. Explain how it can be effectively managed to ensure project success. </s> <s>21. What is project risk management? Discuss how it can be effectively managed to ensure project success. </s> <s>22. Explain the concept of project stakeholder management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>23. Discuss the importance of project integration management in project management. Explain how it can be effectively managed to ensure project success. </s> <s>24. What is project closure? Discuss how it can be effectively managed to ensure project success. </s> <s>25. Explain the concept of project risk management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>26. Discuss the importance of project stakeholder management in project management. Explain how it can be effectively managed to ensure project success. </s> <s>27. What is project integration management? Discuss how it can be effectively managed to ensure project success. </s> <s>28. Explain the concept of project closure in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>29. Discuss the importance of project risk management in project management. Explain how it can be effectively managed to ensure project success. </s> <s>30. Explain the concept of project stakeholder management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>31. Discuss the importance of project integration management in project management. Explain how it can be effectively managed to ensure project success. </s> <s>32. What is project closure? Discuss how it can be effectively managed to ensure project success. </s> <s>33. Explain the concept of project risk management in project management. Discuss how it can be effectively managed to ensure project success. </s> <s>34. Discuss the importance of project stakeholder management in project management. Explain how\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Digital Marketing Textbook: A Comprehensive Guide for Beginners\n",
        "\n",
        "Digital marketing has become an essential part of any business strategy. With the increasing use of technology in marketing, it is crucial for businesses to understand the concepts and techniques of digital marketing. This is where a digital marketing textbook comes in handy. In this article, we will explore the importance of digital marketing textbooks, key concepts, and strategies for effective digital marketing.\n",
        "\n",
        "## Introduction\n",
        "\n",
        "Digital marketing textbooks are a valuable resource for anyone interested in learning about digital marketing. They provide a comprehensive understanding of the concepts, strategies, and tools used in digital marketing. Digital marketing textbooks cover a wide range of topics, including search engine optimization (SEO), social media marketing, content marketing, email marketing, and more. By understanding these concepts, businesses can develop effective digital marketing strategies that drive traffic, generate leads, and increase sales.\n",
        "\n",
        "## Key Concepts in Digital Marketing\n",
        "\n",
        "Digital marketing involves using digital channels to promote products or services. Some of the key concepts in digital marketing include:\n",
        "\n",
        "* **Search engine optimization (SEO):** SEO involves optimizing a website to improve its visibility in search engine results. This includes on-page optimization, off-page optimization, and technical SEO.\n",
        "\n",
        "* **Social media marketing:** Social media marketing involves using social media platforms to promote products or services. This includes creating content, engaging with followers, and running social media ads.\n",
        "\n",
        "* **Content marketing:** Content marketing involves creating and distributing valuable content to attract and engage customers. This includes blog posts, videos, infographics, and other types of content.\n",
        "\n",
        "* **Email marketing:** Email marketing involves sending promotional emails to a targeted audience. This includes creating email campaigns, segmenting email lists, and measuring email marketing metrics.\n",
        "\n",
        "## Strategies for Effective Digital Marketing\n",
        "\n",
        "Digital marketing is a complex field that requires a strategic approach. Here are some strategies for effective digital marketing:\n",
        "\n",
        "* **Define your target audience:** Understanding your target audience is crucial for developing effective digital marketing strategies. This includes identifying their demographics, interests, and behavior.\n",
        "\n",
        "* **Develop a content strategy:** A content strategy involves creating a plan for creating and distributing content. This includes identifying the types of content to create, targeting keywords, and measuring content performance.\n",
        "\n",
        "* **Optimize for search engines:** SEO is an essential part of digital marketing. This includes optimizing website content, building backlinks, and improving website speed.\n",
        "\n",
        "* **Leverage social media:** Social media platforms are a powerful tool for promoting products or services. This includes creating engaging content, running social media ads, and building a community.\n",
        "\n",
        "* **Measure and analyze results:** Measuring and analyzing digital marketing results is crucial for understanding what is working and what needs improvement. This includes tracking website traffic, measuring social media engagement, and analyzing email marketing metrics.\n",
        "\n",
        "## Conclusion\n",
        "\n",
        "Digital marketing textbooks are a valuable resource for anyone interested in learning about digital marketing. They provide a comprehensive understanding of the concepts, strategies, and tools used in digital marketing. By understanding these concepts, businesses can develop effective digital marketing strategies that drive traffic, generate leads, and increase sales."
      ],
      "metadata": {
        "id": "eMj9v9etafnu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Reloading Model in FP16 and Merging with LoRA Weights:"
      ],
      "metadata": {
        "id": "sA3qf1TBYeg_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Empty VRAM\n",
        "del model\n",
        "del pipe\n",
        "del trainer\n",
        "import gc\n",
        "gc.collect()\n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mkQCviG0Zta-",
        "outputId": "a2e7f9c5-63b3-4eea-96be-506385871e5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    low_cpu_mem_usage=True,\n",
        "    return_dict=True,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=device_map,\n",
        ")\n",
        "model = PeftModel.from_pretrained(base_model, new_model)\n",
        "model = model.merge_and_unload()\n",
        "\n",
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "id": "QQn30cRtAZ-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "416537676b504b98aa8569d9e3e25227",
            "ff50fba1010a45c091bd4176ae35c453",
            "42bd3a91d1ae400a9745a00ac31d78f2",
            "4194edbbbc744cf1b1254c2cb262beaa",
            "17b84650a1364ccb90e47cdeb295f10b",
            "2f558167ffa14da9aa50cd6d612dd951",
            "5c8c70bfc89e4d43a368a214f2afe4bb",
            "3bc47fdbfb974b908a0bbdb2ab359a30",
            "dcb1fc3e1c3c4087b10f9fdcdc52fa4d",
            "9bc9acd1cc2b4fdaaa00a1a287698ab7",
            "3c006c7014a5457999eee8649e8c9aa6"
          ]
        },
        "outputId": "7b7880bb-ae2a-48c9-aa9d-b510a2777b09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "416537676b504b98aa8569d9e3e25227"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Uploading Model to Huggingface:"
      ],
      "metadata": {
        "id": "MDSL-GZrYoGU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login\n",
        "\n",
        "model.push_to_hub(new_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(new_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "x-xPb-_qB0dz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513,
          "referenced_widgets": [
            "ea66e94e7f2c4c6d91ff73fa537abd54",
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            "9477cfbea2844f939b9bcbd6e9cd07b7",
            "82d16d427c124200ad361e4c3d04070e",
            "6ccab9ea0b3e4f74a3f10423d6bcbeb9",
            "c5b1443ca36640a5ac53779bd9d7e3ed",
            "2c2cd1db2acc4e41abb0345b17507934",
            "c6ddb24083b2474dbef1f4352e30b1b9",
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            "c9481e224226472bb70756325b3bfd5b",
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            "56687a32cdd541a68b92ef4507701871",
            "330e2a12b9664f76ad69f87a7c82c6a4",
            "8142c5a39a4542d68cf30c37676a3caa",
            "1e2daccfcec044109b40a9a02e2c4d64",
            "40291176efcc4cd7bbe04bebe650c520",
            "85b6f4dd35df4856b0edabc7e544f13b"
          ]
        },
        "outputId": "ee7e40f4-b5b9-4c20-9f9a-a47577c1975b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea66e94e7f2c4c6d91ff73fa537abd54"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
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          "data": {
            "text/plain": [
              "model-00004-of-00004.safetensors:   0%|          | 0.00/1.17G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00003-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ab5786404ae48c081a99891d7598dbc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/aumghag/ft-llama-3.1-8B-capstone-personifAI-edu/commit/bb9553efb65cb8f4825fa872369443f22d187cb5', commit_message='Upload tokenizer', commit_description='', oid='bb9553efb65cb8f4825fa872369443f22d187cb5', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    }
  ]
}