LlamaLoRAOrca.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkAHJMlePhYq",
        "outputId": "6153d50d-e40d-4361-951b-2f4297c306fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.6/227.6 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.20.3)\n",
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            "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)\n",
            "Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.0.1+cu118)\n",
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            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate) (4.7.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.6.0->accelerate) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.6.0->accelerate) (3.25.2)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.6.0->accelerate) (1.3.0)\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.40.0.post4-py3-none-any.whl (101.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.8/101.8 MB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.40.0.post4\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m24.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n",
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            "  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m14.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
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            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.65.0)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
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            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
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            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (3.12.2)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.5.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.13.1 dill-0.3.6 multiprocess-0.70.14 xxhash-3.2.0\n",
            "\u001b[33mWARNING: Skipping wandb as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/peft.git git+https://github.com/huggingface/transformers.git\n",
        "!pip install accelerate\n",
        "!pip install bitsandbytes\n",
        "!pip install sentencepiece\n",
        "!pip install datasets\n",
        "!pip uninstall wandb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "torch.cuda.is_available()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9eJ3B0UBQTgt",
        "outputId": "b8a9b752-cbe7-4f2d-b785-b220c2a041e7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
        "import torch.nn as nn\n",
        "import bitsandbytes as bnb\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"openlm-research/open_llama_3b\",\n",
        "    torch_dtype=torch.float16,\n",
        "    load_in_8bit=True,\n",
        "    device_map='auto',\n",
        ")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"openlm-research/open_llama_3b\")\n",
        "tokenizer.pad_token_id = (\n",
        "    0  # unk. we want this to be different from the eos token\n",
        ")\n",
        "tokenizer.padding_side = \"left\"\n",
        "\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "vZYz4cwwQT-2",
        "outputId": "4fe4a7ec-48f2-45f3-9a5d-42f2f0aaddd2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118_nocublaslt.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so.11.0\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118_nocublaslt.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//172.28.0.1'), PosixPath('8013'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-v100-hm-wje5l52176ub --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: Compute capability < 7.5 detected! Only slow 8-bit matmul is supported for your GPU!\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/506 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "45e9912ee90b476ea33b881716da1e2f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/6.85G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f108bc84a58f445c8f8ce0b795b912a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:accelerate.utils.modeling:The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d266f6517714c35aa08c44f91b8c13c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/593 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bbc52cc4a6864883894479ede8268063"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/534k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "212aa66681094b6bbf806669e96532ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/330 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a6eac4004dd4dd7a046395edf2c9c03"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You are using the legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This means that tokens that come after special tokens will not be properly handled. We recommend you to read the related pull request available at https://github.com/huggingface/transformers/pull/24565\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LlamaForCausalLM(\n",
            "  (model): LlamaModel(\n",
            "    (embed_tokens): Embedding(32000, 3200, padding_idx=0)\n",
            "    (layers): ModuleList(\n",
            "      (0-25): 26 x LlamaDecoderLayer(\n",
            "        (self_attn): LlamaAttention(\n",
            "          (q_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (k_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (v_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (o_proj): Linear8bitLt(in_features=3200, out_features=3200, bias=False)\n",
            "          (rotary_emb): LlamaRotaryEmbedding()\n",
            "        )\n",
            "        (mlp): LlamaMLP(\n",
            "          (gate_proj): Linear8bitLt(in_features=3200, out_features=8640, bias=False)\n",
            "          (down_proj): Linear8bitLt(in_features=8640, out_features=3200, bias=False)\n",
            "          (up_proj): Linear8bitLt(in_features=3200, out_features=8640, bias=False)\n",
            "          (act_fn): SiLUActivation()\n",
            "        )\n",
            "        (input_layernorm): LlamaRMSNorm()\n",
            "        (post_attention_layernorm): LlamaRMSNorm()\n",
            "      )\n",
            "    )\n",
            "    (norm): LlamaRMSNorm()\n",
            "  )\n",
            "  (lm_head): Linear(in_features=3200, out_features=32000, bias=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for param in model.parameters():\n",
        "  param.requires_grad = False  # freeze the model - train adapters later\n",
        "  if param.ndim == 1:\n",
        "    # cast the small parameters (e.g. layernorm) to fp32 for stability\n",
        "    param.data = param.data.to(torch.float32)\n",
        "\n",
        "model.gradient_checkpointing_enable()  # reduce number of stored activations\n",
        "model.enable_input_require_grads()\n",
        "\n",
        "class CastOutputToFloat(nn.Sequential):\n",
        "  def forward(self, x): return super().forward(x).to(torch.float32)\n",
        "model.lm_head = CastOutputToFloat(model.lm_head)"
      ],
      "metadata": {
        "id": "LYqom5agS9Hk"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ],
      "metadata": {
        "id": "ieJo6OuITi71"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import LoraConfig, get_peft_model\n",
        "\n",
        "config = LoraConfig(\n",
        "    r=8,\n",
        "    lora_alpha=16,\n",
        "    target_modules=[\n",
        "    \"q_proj\",\n",
        "    \"v_proj\",\n",
        "],\n",
        "    lora_dropout=0.05,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, config)\n",
        "print_trainable_parameters(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3fTbMuETsgU",
        "outputId": "9cbde0c4-98fd-4944-f116-465231567b3b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 2662400 || all params: 3429136000 || trainable%: 0.07764054852300988\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "qa_dataset = load_dataset(\"Open-Orca/OpenOrca\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263,
          "referenced_widgets": [
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        },
        "id": "k5up5vnxSYOl",
        "outputId": "4f56f232-6f84-4e0f-8438-2a2e0e2b1542"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/6.78k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "865c9aadbc61434f977899b41b2d680e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset parquet/Open-Orca--OpenOrca to /root/.cache/huggingface/datasets/Open-Orca___parquet/Open-Orca--OpenOrca-d0aff81f4c153899/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "214afe00cb5d4847951c48f124aa66a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.01G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "512f047eaf6c485486b8ed065f1ee0fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/3.09G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f92054158e64462b8c129a6e1e77e0cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46108c25846c4824b62b48694ed3e16f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4a2095e447749fc8eac3fd51f0c9b2b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset parquet downloaded and prepared to /root/.cache/huggingface/datasets/Open-Orca___parquet/Open-Orca--OpenOrca-d0aff81f4c153899/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "370413f7d82f406d8709cc04f88fb478"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qa_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDSHKr1tSY8s",
        "outputId": "cf71854e-9f0d-400e-d499-63eda115899c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'system_prompt', 'question', 'response'],\n",
              "        num_rows: 4233923\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def create_prompt(system_prompt: str, question: str, response: str):\n",
        "  if len(response) < 1:\n",
        "    response = \"Cannot Find Answer\"\n",
        "  else:\n",
        "    response = response\n",
        "  prompt_template = f\"### CONTEXT\\n{system_prompt}\\n\\n### QUESTION\\n{question}\\n\\n### RESPONSE\\n{response}</s>\"\n",
        "  return prompt_template"
      ],
      "metadata": {
        "id": "D-JuTD3ZSm6L"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset = qa_dataset[\"train\"].shard(num_shards = 1000, index = 0)\n",
        "train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dpss1rZJSz21",
        "outputId": "52e17152-ba12-46e9-8758-0eb44ebc7cad"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'system_prompt', 'question', 'response'],\n",
              "    num_rows: 4234\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_function(samples):\n",
        "  return tokenizer(create_prompt(samples['system_prompt'], samples['question'], samples['response']), padding='max_length', truncation=True)\n",
        "\n",
        "\n",
        "\n",
        "mapped_train_dataset = train_dataset.map(tokenize_function)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17,
          "referenced_widgets": [
            "41d3fb85f8f340dca9f474c1c3afe33c",
            "8039bb707e7c41ba8b469bd429ccb5b3",
            "8ed5ca64fc9f483f983c34a6a82221ef",
            "702316699a2d4254b72dc4262dce9c38",
            "ce254feb3b0b436ea9d38f2ad30a2242",
            "63576573d322476b9c58150b0badcde1",
            "780c5c58b15e4cd982c1a3dfef8336bb",
            "b4ede51f3b50499bbeb5cfa319f26130",
            "210a4b399ed8455d95fe3624625a4909",
            "2479b0baa5dc4efdaa4e331651daa86b",
            "e265bd92766e4ccbb91ec25997195e9b"
          ]
        },
        "id": "ABxQPsViS36s",
        "outputId": "f2e807b7-f045-4956-ac73-353417dc7bf0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4234 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "41d3fb85f8f340dca9f474c1c3afe33c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapped_train_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dmaew2YpVnL1",
        "outputId": "14606a57-4369-456b-87c5-49736acd2dbd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'system_prompt', 'question', 'response', 'input_ids', 'attention_mask'],\n",
              "    num_rows: 4234\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "\n",
        "trainer = transformers.Trainer(\n",
        "    model=model,\n",
        "    train_dataset=mapped_train_dataset,\n",
        "    args=transformers.TrainingArguments(\n",
        "        per_device_train_batch_size=1,\n",
        "        gradient_accumulation_steps=16,\n",
        "        warmup_steps=100,\n",
        "        max_steps=100,\n",
        "        learning_rate=1e-3,\n",
        "        fp16=True,\n",
        "        logging_steps=1,\n",
        "        output_dir='outputs',\n",
        "    ),\n",
        "    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)\n",
        ")\n",
        "model.config.use_cache = False  # silence the warnings. Please re-enable for inference!"
      ],
      "metadata": {
        "id": "qscIgR7STmLG"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lcMteZcNT2EV",
        "outputId": "178fa023-3a24-469c-d74f-2c82e38d8956"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [100/100 45:46, 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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>2.279700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>2.239400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.969700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>2.481000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>2.363400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>2.154300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>2.046500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>2.220800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>2.032300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>2.258200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>2.267000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>1.898400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>2.136000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>2.043100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.921600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>2.050100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>2.239300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>2.151100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>2.009900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.101400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>2.141800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.921900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.826500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.970300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>2.018700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.931100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.717100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.939000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.665100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.776300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.900200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.697200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.688600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.934400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.639200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.503400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.560600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.644200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.621200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.570500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.649300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>1.612100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.769800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.476800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.731600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.698800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.499900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.575100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.562000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.567600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.551700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.510500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>1.461200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.544800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.555700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.613000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>1.462100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.559900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>1.398900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.671700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>61</td>\n",
              "      <td>1.405400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>62</td>\n",
              "      <td>1.455400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>63</td>\n",
              "      <td>1.620200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>64</td>\n",
              "      <td>1.490300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>65</td>\n",
              "      <td>1.594300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>66</td>\n",
              "      <td>1.454100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>67</td>\n",
              "      <td>1.357300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>68</td>\n",
              "      <td>1.458200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>69</td>\n",
              "      <td>1.660200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.564900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>71</td>\n",
              "      <td>1.324100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>72</td>\n",
              "      <td>1.457000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>73</td>\n",
              "      <td>1.620100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>74</td>\n",
              "      <td>1.546200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.587300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>76</td>\n",
              "      <td>1.413100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>77</td>\n",
              "      <td>1.534600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>78</td>\n",
              "      <td>1.656300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>79</td>\n",
              "      <td>1.372200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.323900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>81</td>\n",
              "      <td>1.479200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>1.649100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>83</td>\n",
              "      <td>1.361100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>84</td>\n",
              "      <td>1.295100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>85</td>\n",
              "      <td>1.532200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>86</td>\n",
              "      <td>1.677400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>87</td>\n",
              "      <td>1.528700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>88</td>\n",
              "      <td>1.568500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>89</td>\n",
              "      <td>1.522700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.409300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>91</td>\n",
              "      <td>1.320000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>92</td>\n",
              "      <td>1.465300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>93</td>\n",
              "      <td>1.505300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>94</td>\n",
              "      <td>1.544400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>95</td>\n",
              "      <td>1.587500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>96</td>\n",
              "      <td>1.531800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>97</td>\n",
              "      <td>1.525900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>98</td>\n",
              "      <td>1.588900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>99</td>\n",
              "      <td>1.512500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.470000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=100, training_loss=1.699730806350708, metrics={'train_runtime': 2778.3049, 'train_samples_per_second': 0.576, 'train_steps_per_second': 0.036, 'total_flos': 6.54062911488e+16, 'train_loss': 1.699730806350708, 'epoch': 0.38})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HUGGING_FACE_USER_NAME = \"Andron00e\""
      ],
      "metadata": {
        "id": "YJjPmVIJVsLg"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "a798091e931b4757968c8e56db155ed9",
            "c0e532bf52144aa0bb652b2cde869bc5",
            "85a2eaf01b544b05a14cf966e98bc78c",
            "41bd53a4d7774d1e8fc8d8e98541f50d",
            "ddd1fbeb50554635a7d3c5ba8be406cb",
            "ccfcebe2efb14c1b92630a7c67e225b8",
            "3f730706d21b48c0a9d749a992b865c5",
            "88c1f4e2c2e94729bf7db0ac1f382ba6",
            "7b2c6748587e488eacba9c1c093ff932",
            "053d83c5c9ef4992af9c0b6d88d45fe1",
            "d6825b50230c4172927adbdbef1f2c49",
            "b3d149f0d51a402395867b53e0a88a50",
            "ee21024e78f444d9b5ed1695bf20e709",
            "7482f12aa3414885adaff9c87209cc1d",
            "cc0a735a6d2243b49f1af9b2dcf86d74",
            "0fa1031d17734a569e9a7337f033671d",
            "e83d4f31f1d64f0da7fab67f3b856c5d",
            "953e0a50e60d4aa48426b2b48419b953",
            "abef9dfca5f84a1ba5eab83f036c4962",
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            "ddb6ad3840d34bab8f570e86f9150761"
          ]
        },
        "id": "g60aMzR_jUqh",
        "outputId": "49304960-2f6b-4e26-da8d-754e8fb288c8"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a798091e931b4757968c8e56db155ed9"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_name = \"Open-Llama-3B-LoRA-OpenOrca\"\n",
        "\n",
        "model.push_to_hub(f\"{HUGGING_FACE_USER_NAME}/{model_name}\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "7ec90d427cea4dca8a03ba70f1d9344f",
            "874344b276af4c8a9e45acd5e853c3e1",
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            "654ff70c35cd4779b4ad918810a55db8",
            "24df696fcea344cda2c210342764e214",
            "c5e49ac224474a82a716f2ee975d1692"
          ]
        },
        "id": "gKRHKK1UjCPw",
        "outputId": "f2623e92-1b64-4c29-a9f3-24232603cdd9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.bin:   0%|          | 0.00/10.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ec90d427cea4dca8a03ba70f1d9344f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/Andron00e/Open-Llama-3B-LoRA-OpenOrca/commit/a9fb2c4e19dbcdd6b445df4e78a10392c548460d', commit_message='Upload model', commit_description='', oid='a9fb2c4e19dbcdd6b445df4e78a10392c548460d', pr_url=None, pr_revision=None, pr_num=None)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = f\"{HUGGING_FACE_USER_NAME}/{model_name}\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n",
        "\n",
        "\n",
        "qa_model = PeftModel.from_pretrained(model, peft_model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98,
          "referenced_widgets": [
            "3620e4d04d7d472984c478e21067f08e",
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            "c21fc2e0d6954c7a871ed6234ac1a030",
            "a8af3b45bc5947e1a3416d5e05db7d71",
            "e89011887a7b4621bd2b20471ba669ee",
            "e943b82149a44e05aabfc1b0500d0eec",
            "af35e4f03b114b619076573ccb13ba4b",
            "d085d9b00a394c9bb006cd97f859653e",
            "61cb546ff2034127a495b0cb28968d37",
            "937acb8b7b054a549a0b580b8a1c69fe"
          ]
        },
        "id": "_4f_svHtjObM",
        "outputId": "2a943d37-6987-483b-d6ac-ee54608df646"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/adapter_config.json:   0%|          | 0.00/427 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3620e4d04d7d472984c478e21067f08e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:accelerate.utils.modeling:The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/10.7M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5d6702ec2568407fbfeadec1a79bdc1b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "def make_inference(system_prompt, question):\n",
        "  batch = tokenizer(f\"### CONTEXT\\n{system_prompt}\\n\\n### QUESTION\\n{question}\\n\\n### RESPONSE\\n\", return_tensors='pt')\n",
        "\n",
        "  with torch.cuda.amp.autocast():\n",
        "    output_tokens = qa_model.generate(**batch, max_new_tokens=200)\n",
        "\n",
        "  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))"
      ],
      "metadata": {
        "id": "vrVKmAPdjsYO"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Cheese is the best food.\"\n",
        "question = \"What is the best food?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "cYbJJXfCkm8w",
        "outputId": "316b1c71-1b84-493c-c9cf-99380e50ce7f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1468: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nCheese is the best food.\n\n### QUESTION\nWhat is the best food?\n\n### RESPONSE\nThe best food is cheese!"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Teach me how to fine-tune open llama with LoRA.\"\n",
        "question = \"How to fine-tune open llama with LoRA?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "EVDOJFF0k4uj",
        "outputId": "b28b1339-4c94-4394-b7f9-3b82621b3c99"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nTeach me how to fine-tune open llama with LoRA.\n\n### QUESTION\nHow to fine-tune open llama with LoRA?\n\n### RESPONSE\nOpenLlama is a machine learning library that can be used to build and train models for a variety of tasks. LoRA is a machine learning framework that provides a set of tools and libraries for building and training models.\n\nTo fine-tune OpenLlama with LoRA, you will need to install the LoRA library and the OpenLlama library. You will also need to install the TensorFlow library, which is a popular machine learning library.\n\nOnce you have installed the required libraries, you can start fine-tuning OpenLlama with LoRA. To do this, you will need to create a LoRA model and train it with OpenLlama.\n\nTo create a LoRA model, you will need to create a LoRA model object. This object will contain the parameters that you will use to train the model. You will also need to provide a training dataset and a training objective.\n\nTo train the Lo"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BO69kgGHlVFu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}