pythia_2_8b_finetuning.ipynb

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            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "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 (14.1 MB)\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 (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.10.0->accelerate)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
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            "  Downloading shtab-1.7.1-py3-none-any.whl (14 kB)\n",
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            "  Downloading dill-0.3.8-py3-none-any.whl (116 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m17.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting xxhash (from datasets->trl)\n",
            "  Downloading xxhash-3.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets->trl)\n",
            "  Downloading multiprocess-0.70.16-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->trl) (3.9.5)\n",
            "Collecting huggingface-hub (from accelerate)\n",
            "  Downloading huggingface_hub-0.22.2-py3-none-any.whl (388 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.9/388.9 kB\u001b[0m \u001b[31m46.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl) (1.3.1)\n",
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            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl) (2.16.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate) (2.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl) (2023.4)\n",
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            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets->trl) (1.16.0)\n",
            "Installing collected packages: xxhash, shtab, 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, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface-hub, tyro, nvidia-cusolver-cu12, datasets, bitsandbytes, accelerate, trl, peft\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.20.3\n",
            "    Uninstalling huggingface-hub-0.20.3:\n",
            "      Successfully uninstalled huggingface-hub-0.20.3\n",
            "Successfully installed accelerate-0.29.3 bitsandbytes-0.43.1 datasets-2.19.0 dill-0.3.8 huggingface-hub-0.22.2 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-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 shtab-1.7.1 trl-0.8.5 tyro-0.8.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip3 install accelerate peft bitsandbytes transformers trl"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "id": "Onpcbr19tpuo",
        "outputId": "0e44a555-48f1-456b-d6b7-5274abfe874b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "d64d23fa715b492dbf72ca2a2fb7d384",
            "8b91f4deac87471ca2388a3900c1265d",
            "b1a704c5ca5a45e7bc700c42faf69ec8",
            "d1f951bbb00146fd9d598c09c4a0affe",
            "fac67ebbcc7e42bbb8e75141909034d3",
            "f8f408fd73fc47a3bb1cab4fcf526873",
            "39e2b30dbac94939944539a063a02752",
            "f3a307b785944f0bbc26d5cc7fd883be",
            "b574a4b399234dd8af2b2717462e6e63",
            "97fb907d34a74aa5aceb6985f667304a",
            "34502bddf79845dab41c94ed1a5386ac",
            "e76944cdd7c940959c3135c1986d5bec",
            "d174175bd8d14eca97b6e24291a52857",
            "a8ec40ed5fc34686aee7465541627b2c",
            "189dbe7c451a4e2b9d2dbe853c6b9eda",
            "1d2f719a7b5d4b478745506eb7cd49e7",
            "00a4c1d951db4ddf9050a3337d37e73f",
            "8bc4eeb291784df894d96cc8732c71ca",
            "ff7184cf7fc343f3bae0508d0c5998e2",
            "8a3b0d642ff04b8fa5a74d350d2a580b",
            "e16c2dcf20cf4f5598456154c25c2204",
            "010876cf5e6740ed86df146882149edc",
            "a4924eeb4b384162bd61827a3d0f4328",
            "81ed35af253a431fb72512ea3b1a28d3",
            "698283b8504d413b9a33f279c6d5bcc2",
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            "1d71c2a0e75f48798c543c6950f60884",
            "2288e4155e2841e9b7ec1c57f93ccd52",
            "906c07c66ac74ce4be9c4aa44e93f96b",
            "8d12cdf9e57e4f69b82c6994de878bff",
            "623b42c67d40447585980bcfc6d85f21"
          ]
        }
      },
      "execution_count": 2,
      "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": "d64d23fa715b492dbf72ca2a2fb7d384"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load the required packages.\n",
        "import torch\n",
        "from datasets import load_dataset, Dataset\n",
        "from peft import LoraConfig, AutoPeftModelForCausalLM\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments\n",
        "from trl import SFTTrainer\n",
        "import os"
      ],
      "metadata": {
        "id": "8mnl7YgdtxAb"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=\"mudata.json\"\n",
        "model_id=\"EleutherAI/pythia-2.8b\"\n",
        "output_model=\"pythia-2.8b-vetdataset-finetuned-v2\""
      ],
      "metadata": {
        "id": "4Ho3JJDLt6Pa"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data preparation"
      ],
      "metadata": {
        "id": "GnimXYnRvR9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "# Read the original JSON file\n",
        "with open('mudata.json', 'r') as file:\n",
        "    data = json.load(file)\n",
        "\n",
        "# Add IDs to each row\n",
        "for idx, item in enumerate(data, start=1):\n",
        "    item['id'] = idx\n",
        "\n",
        "# Write the updated data to a new file\n",
        "new_file_path = 'updated_data.json'\n",
        "with open(new_file_path, 'w') as new_file:\n",
        "    json.dump(data, new_file, indent=2)\n",
        "\n",
        "print(f'Updated data with IDs written to {new_file_path}')"
      ],
      "metadata": {
        "id": "28VnkJnUlyFa",
        "outputId": "59d31974-1a62-4111-a3ef-53030a51b63e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated data with IDs written to updated_data.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "# Load dataset from the hub\n",
        "dataset = load_dataset(\"json\", data_files=\"updated_data.json\")\n",
        "\n",
        "datasets_train_test = dataset[\"train\"].train_test_split(test_size=150)\n",
        "datasets_train_validation = datasets_train_test[\"train\"].train_test_split(test_size=150)\n",
        "\n",
        "dataset[\"train\"] = datasets_train_validation[\"train\"]\n",
        "dataset[\"validation\"] = datasets_train_validation[\"test\"]\n",
        "dataset[\"test\"] = datasets_train_test[\"test\"]\n",
        "\n",
        "print(f\"Train dataset size: {len(dataset['train'])}\")\n",
        "print(f\"Test dataset size: {len(dataset['test'])}\")\n",
        "\n",
        "# Train dataset size: 14732\n",
        "# Test dataset size: 819"
      ],
      "metadata": {
        "id": "-hRThMyilzRK",
        "outputId": "d6e1e920-3f02-452c-e7c0-58c28f1813c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "b1e5e0bafaa4446aaab19b59b984be7d",
            "bee85909c6dc4f0ebb6efa4e52ac2d94",
            "a923610428ee43d0886dd2c1b4c5ad8a",
            "f9cd7c7b8da64f3f86f25828bdb501b9",
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            "8ddb73f9b11441c7a60835e48391f653",
            "3c012240da79420c80f2b7c34ec01e03"
          ]
        }
      },
      "execution_count": 6,
      "outputs": [
        {
          "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": "b1e5e0bafaa4446aaab19b59b984be7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train dataset size: 5732\n",
            "Test dataset size: 150\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model the Model (not the base version)"
      ],
      "metadata": {
        "id": "Mnej4AYwvawA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_model_and_tokenizer(mode_id):\n",
        "\n",
        "    tokenizer = AutoTokenizer.from_pretrained(mode_id)\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=\"float16\", bnb_4bit_use_double_quant=True\n",
        "    )\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        mode_id, quantization_config=bnb_config, device_map=\"auto\"\n",
        "    )\n",
        "    model.config.use_cache=False\n",
        "    model.config.pretraining_tp=1\n",
        "    return model, tokenizer"
      ],
      "metadata": {
        "id": "WrniDfzlvNd7"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install -i https://test.pypi.org/simple/bitsandbytes"
      ],
      "metadata": {
        "id": "BaD23-Fev7sK"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model, tokenizer = get_model_and_tokenizer(model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319,
          "referenced_widgets": [
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            "ac16273dbd1a468387f875e284c82a0f",
            "1b2a746e46c9401ab0385574ee6176a2"
          ]
        },
        "id": "ogWCiBBzvk1C",
        "outputId": "e5c228b4-d961-4494-aada-1981c100c391"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/396 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5abcb63cc1e842cca5055c7cfe4c7e75"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.11M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8405e14255954da9bd363d3fd6f94b82"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/99.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8ed6a273032046d6866bdf7500072346"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/571 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7a9b0bb90214c22b35569197bd0b4b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.68G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "214f5da5810c4a778bb35ebd75545345"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import concatenate_datasets\n",
        "\n",
        "# The maximum total input sequence length after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\n",
        "tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"question\"], truncation=True), batched=True)\n",
        "max_source_length = max([len(x) for x in tokenized_inputs[\"input_ids\"]])\n",
        "print(f\"Max source length: {max_source_length}\")\n",
        "\n",
        "# The maximum total sequence length for target text after tokenization.\n",
        "# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n",
        "tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"answer\"], truncation=True), batched=True)\n",
        "max_target_length = max([len(x) for x in tokenized_targets[\"input_ids\"]])\n",
        "print(f\"Max target length: {max_target_length}\")\n"
      ],
      "metadata": {
        "id": "ygt68NwWmMWs",
        "outputId": "56ec6ac7-075c-46d6-a0d8-07253190f746",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
            "160e27d2b6ba40129d294c44714f9ba6",
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            "5e91a2d88fbd4771a24f3b8e12ddd665"
          ]
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5882 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "160e27d2b6ba40129d294c44714f9ba6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max source length: 51\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5882 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38e941ee352e48c39192858154d43307"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max target length: 126\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_function(sample,padding=\"max_length\"):\n",
        "    # add prefix to the input for t5\n",
        "    inputs = [\"answer the following question: \" + item for item in sample[\"question\"]]\n",
        "\n",
        "    # tokenize inputs\n",
        "    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n",
        "\n",
        "    # Tokenize targets with the `text_target` keyword argument\n",
        "    labels = tokenizer(text_target=sample[\"answer\"], max_length=max_target_length, padding=padding, truncation=True)\n",
        "\n",
        "    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n",
        "    # padding in the loss.\n",
        "    if padding == \"max_length\":\n",
        "        labels[\"input_ids\"] = [\n",
        "            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n",
        "        ]\n",
        "\n",
        "    model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "tokenized_dataset = dataset.map(preprocess_function, batched=True)\n",
        "print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")"
      ],
      "metadata": {
        "id": "YpCmKE10mp95",
        "outputId": "038cf39b-9ccb-4fb2-9309-a1946e564f81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 130,
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          ]
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ec4b7fa8406412795a8dcb5aab76b6f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "2ff34cd202df446d9c5d9512c3d95cf2"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Keys of tokenized dataset: ['question', 'id', 'answer', 'input_ids', 'attention_mask', 'labels']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_dataset = tokenized_dataset.map(lambda example: {'text': example['question'] + example['answer']})"
      ],
      "metadata": {
        "id": "1Tb5o7upmTDg",
        "outputId": "c24dbda2-22c0-46fa-bffc-128015405c7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113,
          "referenced_widgets": [
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            "0133f0c927054eca9d6807c21393e097",
            "11fc538e17aa45638799534858fbdfc7",
            "bcd14bcdec9940f5ae714f36f608be8d",
            "99518616afc94d0c88745adf369b60f0"
          ]
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5732 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f2c9b3ab1cac47f39e0cb7dfb3eb8a9f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef76a43c9b80476484057d9d1cb4d5de"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/150 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6fbfc7c454045098e7724fc5513ec0c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Setting up the LoRA"
      ],
      "metadata": {
        "id": "d2hOfQBZw_YX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "peft_config = LoraConfig(\n",
        "        r=8, lora_alpha=16, lora_dropout=0.05, bias=\"none\", task_type=\"CAUSAL_LM\"\n",
        "    )"
      ],
      "metadata": {
        "id": "JAGakLbmvtj6"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_arguments = TrainingArguments(\n",
        "        output_dir=output_model,\n",
        "        per_device_train_batch_size=16,\n",
        "        gradient_accumulation_steps=4,\n",
        "        optim=\"paged_adamw_32bit\",\n",
        "        learning_rate=2e-4,\n",
        "        lr_scheduler_type=\"cosine\",\n",
        "        save_strategy=\"epoch\",\n",
        "        logging_steps=10,\n",
        "        num_train_epochs=3,\n",
        "        max_steps=250,\n",
        "        fp16=True,\n",
        "        push_to_hub=True\n",
        "    )"
      ],
      "metadata": {
        "id": "Suz5-CbVxDGm"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = SFTTrainer(\n",
        "        model=model,\n",
        "        train_dataset=tokenized_dataset[\"train\"],\n",
        "        peft_config=peft_config,\n",
        "        dataset_text_field=\"text\",\n",
        "        args=training_arguments,\n",
        "        tokenizer=tokenizer,\n",
        "        packing=False,\n",
        "        max_seq_length=1024\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7KtgzA8xGVs",
        "outputId": "f066eed2-9859-4a74-fa11-36b60fc7873f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches', 'even_batches', 'use_seedable_sampler']). Please pass an `accelerate.DataLoaderConfiguration` instead: \n",
            "dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False, even_batches=True, use_seedable_sampler=True)\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "nXOXGKK0xU6X",
        "outputId": "7e7aa712-dad3-4e53-8d8d-ea735c10d614"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 14:55, Epoch 2/3]\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>10</td>\n",
              "      <td>2.823200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>2.323100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>2.125800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>2.031100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.936800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.931300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>70</td>\n",
              "      <td>1.857500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>80</td>\n",
              "      <td>1.780400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>1.726800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.695800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>110</td>\n",
              "      <td>1.700200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>1.620000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>130</td>\n",
              "      <td>1.663900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>140</td>\n",
              "      <td>1.653100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.645400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>160</td>\n",
              "      <td>1.650800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>170</td>\n",
              "      <td>1.668500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>180</td>\n",
              "      <td>1.559500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>190</td>\n",
              "      <td>1.544700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.534700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>210</td>\n",
              "      <td>1.556200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>220</td>\n",
              "      <td>1.584000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>230</td>\n",
              "      <td>1.583100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>240</td>\n",
              "      <td>1.549000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.528900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=250, training_loss=1.7709535064697266, metrics={'train_runtime': 900.3516, 'train_samples_per_second': 17.771, 'train_steps_per_second': 0.278, 'total_flos': 1.295030473629696e+16, 'train_loss': 1.7709535064697266, 'epoch': 2.79})"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save trained model\n",
        "trainer.model.save_pretrained(output_model)"
      ],
      "metadata": {
        "id": "VwKEgchY103o"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging the LoRA with the base model"
      ],
      "metadata": {
        "id": "Z1DJz6Zr4Jke"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import AutoPeftModelForCausalLM, PeftModel\n",
        "from transformers import AutoModelForCausalLM\n",
        "import torch\n",
        "import os\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, load_in_8bit=False,\n",
        "                                             device_map=\"auto\",\n",
        "                                             trust_remote_code=True)\n",
        "\n",
        "model_path = \"/content/dolly-v2-3b-vetdataset-finetuned-v2\"\n",
        "\n",
        "peft_model = PeftModel.from_pretrained(base_model, output_model, from_transformers=True, device_map=\"auto\")\n",
        "\n",
        "model = peft_model.merge_and_unload()"
      ],
      "metadata": {
        "id": "V6QKygPu4E91"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.push_to_hub(output_model, use_temp_dir=False)\n",
        "tokenizer.push_to_hub(output_model, use_temp_dir=False)"
      ],
      "metadata": {
        "id": "mudcKgOknSLX",
        "outputId": "0f5d1c1e-a2c7-4e51-a520-b7b703d51666",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
            "1c03b8faa8fb4982b1539e5f5c54094b",
            "7a94b011c0094f30949d5a27995bf6ee",
            "342e2c0a7ad74d71bab6a6c14cf17a66",
            "90bcf3765a314d00883f826e01b72ae4",
            "697b6ff6d497482297ebffbb05c47c92",
            "0499030adfbc4359ab112f38cc3d4077",
            "0f37c292579f45bfb0732c2c262e2c78",
            "afce5ef63f6549a496f765848e69cb96",
            "58c2ea44a92a4dc2b766a3137649c76f",
            "ad308060cfa6416294390d3ed23b7ef0",
            "df5776eb216c4039a5553bf506919261",
            "d91dede789b0406087e7ededcd4fb45b",
            "b65c8185ed294845a0f5422ffc1e42f5",
            "8fd541c60f7d47a6b7614d460c9f2ed7",
            "ff4dd84f8f6642108c314b030717b467",
            "419c4296e5fb429bb59e20382a321244",
            "e2404b0d2f3443ea942df63d781980b7",
            "8ed0cd32ba0c48d5b597aabb7c61b111",
            "f9598230bba049149d4b5ae9e83187b9",
            "d67c6075c9d44827b4c422a51edbaeb9",
            "e4645ac252354ae8a3bfc045497bf4cc",
            "a9fbc8e024834aa3893113a9c282a1eb",
            "f989423226324e1ba0c5d798714ad0a4",
            "c35e3677662a40fd9d3de300d7500b0d",
            "02c6b7cff7954fb682ccbf64397620c0",
            "d15cad6d50ab4b5691faa021f82b6bee",
            "0de8b6f88e334311b0d185a65176388a",
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            "785db81325874412a46bd91ad6708cbe",
            "41de3b7298ff4fb79dae7c579262c18e",
            "10f4c99c51ec49de8fb35b12b69173a7",
            "34094f486637455dae2bd451d9b69fcd",
            "4b79ae5aafca4f65beafac34215bf7c6",
            "d29b5403851a47dfa75290325a9bd6b2",
            "abb3987621bb48b2b187eb31c4212ee4",
            "84f3babf6dab4982b047eb79361b434d",
            "53e7c16b8dd14b7dadce3a4060ded1aa",
            "3efcccf62c024d30a40a1248229f15e5",
            "436a86b322f8473aad6bb9ecd5f1e6ef",
            "a58605b6f8884fce9e375633a5367d4b",
            "c1f3a1b8fd60426da8a26b45007d978e",
            "0af00b53b6864e83a322cb0d35078b43",
            "dc4a169c592b4851a9ab98acdb203dbe",
            "8e6c184cf58a43f68e6a62a31bf20e5f"
          ]
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c03b8faa8fb4982b1539e5f5c54094b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/572M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d91dede789b0406087e7ededcd4fb45b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f989423226324e1ba0c5d798714ad0a4"
            }
          },
          "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": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d29b5403851a47dfa75290325a9bd6b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/ahmed807762/pythia-2.8b-vetdataset-finetuned-v2/commit/78024a324d095025bc47995bdb7f3d09167dbf9b', commit_message='Upload tokenizer', commit_description='', oid='78024a324d095025bc47995bdb7f3d09167dbf9b', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU2EIsuI4v50",
        "outputId": "8177190e-952d-4a22-da14-c2bd6f8d7a0c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GPTNeoXForCausalLM(\n",
              "  (gpt_neox): GPTNeoXModel(\n",
              "    (embed_in): Embedding(50304, 2560)\n",
              "    (emb_dropout): Dropout(p=0.0, inplace=False)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x GPTNeoXLayer(\n",
              "        (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
              "        (post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
              "        (post_attention_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (post_mlp_dropout): Dropout(p=0.0, inplace=False)\n",
              "        (attention): GPTNeoXAttention(\n",
              "          (rotary_emb): GPTNeoXRotaryEmbedding()\n",
              "          (query_key_value): Linear(in_features=2560, out_features=7680, bias=True)\n",
              "          (dense): Linear(in_features=2560, out_features=2560, bias=True)\n",
              "          (attention_dropout): Dropout(p=0.0, inplace=False)\n",
              "        )\n",
              "        (mlp): GPTNeoXMLP(\n",
              "          (dense_h_to_4h): Linear(in_features=2560, out_features=10240, bias=True)\n",
              "          (dense_4h_to_h): Linear(in_features=10240, out_features=2560, bias=True)\n",
              "          (act): GELUActivation()\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (final_layer_norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (embed_out): Linear(in_features=2560, out_features=50304, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Inference from the LLM"
      ],
      "metadata": {
        "id": "n1nOANDaynhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/ahmed807762/pythia-2.8b-vetdataset-finetuned-v2\"\n",
        "headers = {\"Authorization\": \"Bearer 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\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\n",
        "output = query({\n",
        "\t\"inputs\": \"Question: How can I prevent foot rot in my cows? Answer: \",\n",
        "  \"options\": {\"wait_for_model\": True},\n",
        "  \"parameters\": {\"max_new_tokens\": 250}\n",
        "})"
      ],
      "metadata": {
        "id": "rAyggZcjoEmW"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdLnBObCoYVV",
        "outputId": "26553065-1ea8-484d-90f9-9c3b3a759e44"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'error': 'Could not load model ahmed807762/pythia-2.8b-vetdataset-finetuned-v2 with any of the following classes: (<class \\'transformers.models.gpt_neox.modeling_gpt_neox.GPTNeoXForCausalLM\\'>,). See the original errors:\\n\\nwhile loading with GPTNeoXForCausalLM, an error is thrown:\\nTraceback (most recent call last):\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py\", line 286, in hf_raise_for_status\\n    response.raise_for_status()\\n  File \"/usr/local/lib/python3.11/site-packages/requests/models.py\", line 1021, in raise_for_status\\n    raise HTTPError(http_error_msg, response=self)\\nrequests.exceptions.HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/EleutherAI/pythia-2.8b/resolve/78024a324d095025bc47995bdb7f3d09167dbf9b/config.json\\n\\nThe above exception was the direct cause of the following exception:\\n\\nTraceback (most recent call last):\\n  File \"/src/transformers/src/transformers/utils/hub.py\", line 398, in cached_file\\n    resolved_file = hf_hub_download(\\n                    ^^^^^^^^^^^^^^^^\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\\n    return fn(*args, **kwargs)\\n           ^^^^^^^^^^^^^^^^^^^\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/file_download.py\", line 1238, in hf_hub_download\\n    metadata = get_hf_file_metadata(\\n               ^^^^^^^^^^^^^^^^^^^^^\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\\n    return fn(*args, **kwargs)\\n           ^^^^^^^^^^^^^^^^^^^\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/file_download.py\", line 1631, in get_hf_file_metadata\\n    r = _request_wrapper(\\n        ^^^^^^^^^^^^^^^^^\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/file_download.py\", line 385, in _request_wrapper\\n    response = _request_wrapper(\\n               ^^^^^^^^^^^^^^^^^\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/file_download.py\", line 409, in _request_wrapper\\n    hf_raise_for_status(response)\\n  File \"/usr/local/lib/python3.11/site-packages/huggingface_hub/utils/_errors.py\", line 296, in hf_raise_for_status\\n    raise EntryNotFoundError(message, response) from e\\nhuggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: Root=1-66259e18-407ba01f119667bb1d9501b1;25e36ba3-2096-49f7-b428-037a7298fe9c)\\n\\nEntry Not Found for url: https://huggingface.co/EleutherAI/pythia-2.8b/resolve/78024a324d095025bc47995bdb7f3d09167dbf9b/config.json.\\n\\nThe above exception was the direct cause of the following exception:\\n\\nTraceback (most recent call last):\\n  File \"/src/transformers/src/transformers/pipelines/base.py\", line 279, in infer_framework_load_model\\n    model = model_class.from_pretrained(model, **kwargs)\\n            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \"/src/transformers/src/transformers/modeling_utils.py\", line 2983, in from_pretrained\\n    config, model_kwargs = cls.config_class.from_pretrained(\\n                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \"/src/transformers/src/transformers/configuration_utils.py\", line 602, in from_pretrained\\n    config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)\\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \"/src/transformers/src/transformers/configuration_utils.py\", line 631, in get_config_dict\\n    config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)\\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n  File \"/src/transformers/src/transformers/configuration_utils.py\", line 686, in _get_config_dict\\n    resolved_config_file = cached_file(\\n                           ^^^^^^^^^^^^\\n  File \"/src/transformers/src/transformers/utils/hub.py\", line 452, in cached_file\\n    raise EnvironmentError(\\nOSError: EleutherAI/pythia-2.8b does not appear to have a file named config.json. Checkout \\'https://huggingface.co/EleutherAI/pythia-2.8b/78024a324d095025bc47995bdb7f3d09167dbf9b\\' for available files.\\n\\n\\n'}"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import GenerationConfig\n",
        "from time import perf_counter\n",
        "\n",
        "def generate_response(user_input):\n",
        "\n",
        "  prompt = formatted_prompt(user_input)\n",
        "\n",
        "  inputs = tokenizer([prompt], return_tensors=\"pt\")\n",
        "  generation_config = GenerationConfig(penalty_alpha=0.6,do_sample = True,\n",
        "      top_k=5,temperature=0.5,repetition_penalty=1.2,\n",
        "      max_new_tokens=12,pad_token_id=tokenizer.eos_token_id\n",
        "  )\n",
        "  start_time = perf_counter()\n",
        "\n",
        "  inputs = tokenizer(prompt, return_tensors=\"pt\").to('cuda')\n",
        "\n",
        "  outputs = model.generate(**inputs, generation_config=generation_config)\n",
        "  print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n",
        "  output_time = perf_counter() - start_time\n",
        "  print(f\"Time taken for inference: {round(output_time,2)} seconds\")"
      ],
      "metadata": {
        "id": "mWKVO_5j5pSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def formatted_prompt(question)-> str:\n",
        "    return f\"<|im_start|>user\\n{question}<|im_end|>\\n<|im_start|>assistant:\""
      ],
      "metadata": {
        "id": "yIT0IuT9HtEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_color_space(hex_color):\n",
        "    def hex_to_rgb(hex_color):\n",
        "        hex_color = hex_color.lstrip('#')\n",
        "        return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4))\n",
        "    r, g, b = hex_to_rgb(hex_color)\n",
        "    print(f'{hex_color}: \\033[48;2;{r};{g};{b}m           \\033[0m')"
      ],
      "metadata": {
        "id": "cbRpHaNwHthY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(user_input='Light Orange color')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FeisorS5pZD",
        "outputId": "83830eb7-8cf2-450b-dd19-cb310c12237b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user\n",
            "Light Orange color \n",
            " assistant: #f0901a \n",
            " light\n",
            "Time taken for inference: 0.35 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print_color_space('#f0901a ')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QvuqsFit5pcU",
        "outputId": "461399d8-0eac-4f80-80be-777432aeedf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#f0901a : \u001b[48;2;240;144;26m           \u001b[0m\n"
          ]
        }
      ]
    }
  ]
}