phi_2_offline_version.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cobusgreyling/hugging-face/blob/main/phi_2_offline_version.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "byYS5nbK6KIc",
        "outputId": "379333f8-b5b3-45cd-c6cd-3c88def07bff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers\n",
            "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-20d37fmh\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-20d37fmh\n",
            "  Resolved https://github.com/huggingface/transformers to commit 3e70a207dfa6408c440042f2f8076dd6bfb43e8b\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",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (0.15.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (0.4.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.38.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.0.dev0) (4.9.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.38.0.dev0) (2024.2.2)\n"
          ]
        }
      ],
      "source": [
        "pip install git+https://github.com/huggingface/transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Y_Tw0bXxXryO",
        "outputId": "e0a1605a-da91-4a6b-f25f-7660e04f23e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (0.1.99)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.27.0)\n",
            "Requirement already satisfied: einops in /usr/local/lib/python3.10/dist-packages (0.7.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from accelerate) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (23.2)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from accelerate) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.10.0 in /usr/local/lib/python3.10/dist-packages (from accelerate) (2.1.0+cu121)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.20.3)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from accelerate) (0.4.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (4.9.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate) (2.1.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate) (4.66.1)\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: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub->accelerate) (2024.2.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate) (1.3.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install sentencepiece accelerate einops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "79xL7PfrB_JN"
      },
      "outputs": [],
      "source": [
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "DSWGI2ZVCCaD",
        "outputId": "a5ed1fbd-baf5-46d5-a7c9-c889a94a6b55",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "torch.cuda.is_available()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "1VIhCuUm5sTk"
      },
      "outputs": [],
      "source": [
        "from huggingface_hub import snapshot_download"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "x5eWWvNs5wJ2",
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          "base_uri": "https://localhost:8080/",
          "height": 761,
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            "730663fb4e134c85b26d97be08f2fe7c"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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"
          ]
        },
        {
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6319191de554ab38ed0312137cb910d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1bed696e3d7049a1a589896d69c3fc9c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76ea7c8d3df84ed385a7b1f02ae4f641"
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e7e3ba3a46841279769bbcd723c3e19"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a5fed1acbcb4def82fd8d029abd935b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/564M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bda6e5059fd24538b760e113160a3d8c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "repo_id = 'microsoft/phi-2'\n",
        "\n",
        "model_path = snapshot_download(repo_id=repo_id,repo_type=\"model\", local_dir=\"./phi-2\", local_dir_use_symlinks=False)\n",
        "\n",
        "model_path = \"./phi-2\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NFrzMlTI4r2L"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "C7_HE8hc4n3P",
        "outputId": "ce56ca86-5c98-4bf6-aef8-a5d5aea8f650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e537f2b8779d4f8e9faf25be1692ed42",
            "9a1ff82c4b1b4a15afa9951885774b8a",
            "0b8bdc4849914df194ceaaee6f6f7d7a",
            "676f09bfc87e4b0c8efc524c0ec9227b",
            "4b05550a63374e55ac734bc2d8e74bba",
            "1573de514c034a658bfd74a8fa16543f",
            "79e5c4d1863b493a9d49399fe831fa1c",
            "fe6402b7cb204a249fcc03b5600d8a2f",
            "ac7a61192475462db5c7d73cde410dcc",
            "d2877589c144472fbbcbb56d6117e61f",
            "542ad0102c194e629932d2cdf2e02768"
          ]
        }
      },
      "outputs": [
        {
          "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": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e537f2b8779d4f8e9faf25be1692ed42"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)\n",
        "\n",
        "# Needs 12GB of vRAM to run in float32 (default)\n",
        "# Run this line to load in float16. You need Gb of vRAM\n",
        "torch.set_default_dtype(torch.float16)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(model_path, device_map=\"auto\", trust_remote_code=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "ZJtUyyRAPhsD",
        "outputId": "87ffa009-de51-4f4e-9c1f-77a932c88472",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "device(type='cuda', index=0)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "model.device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "7qn-Qp1PNFyA"
      },
      "outputs": [],
      "source": [
        "import time\n",
        "def generate(prompt: str, generation_params: dict = {\"max_length\":200})-> str :\n",
        "    s = time.time()\n",
        "\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
        "    outputs = model.generate(**inputs, **generation_params)\n",
        "    completion = tokenizer.batch_decode(outputs)[0]\n",
        "\n",
        "    elapsed = time.time() - s\n",
        "\n",
        "    num_input_tokens = inputs['input_ids'].shape[1]\n",
        "    num_total_tokens = outputs.shape[1]\n",
        "    num_output_tokens = float(num_total_tokens) - num_input_tokens\n",
        "    speed = num_output_tokens / elapsed\n",
        "\n",
        "    print(f\"Took {round(elapsed,1)} seconds to generate {int(num_output_tokens)} new tokens at speed {round(speed, 1)} tokens/seconds\")\n",
        "\n",
        "    return completion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "iwWmJxQMNU7j"
      },
      "outputs": [],
      "source": [
        "prompt = \"\"\"\n",
        "Create a list of 5 words which have a similar meating to the word hope.\n",
        "\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "WpWkxmqKZcU5",
        "outputId": "2dd3c537-31e1-42a6-80b3-134d7d2b72d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Took 7.3 seconds to generate 180 new tokens at speed 24.7 tokens/seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nCreate a list of 5 words which have a similar meating to the word hope. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to hope\\nwords = ['optimism', 'faith', 'aspiration', 'desire', 'expectation']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['optimism', 'faith', 'aspiration', 'desire', 'expectation']\\n```\\n\\nExercise 2:\\n\\nCreate a list of 5 words which have a similar meaning to the word despair. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to despair\\nwords = ['desolation', 'despair', 'anguish', 'despair', 'despair']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "result = generate(prompt, generation_params={\"max_length\":200})\n",
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "Y6P9CGTMY2j9",
        "outputId": "3d8cf912-3997-4fd4-b2a6-4ca0d18e8980"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Took 7.1 seconds to generate 180 new tokens at speed 25.3 tokens/seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nCreate a list of 5 words which have a similar meating to the word hope. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to hope\\nwords = ['optimism', 'faith', 'aspiration', 'desire', 'expectation']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['optimism', 'faith', 'aspiration', 'desire', 'expectation']\\n```\\n\\nExercise 2:\\n\\nCreate a list of 5 words which have a similar meaning to the word despair. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to despair\\nwords = ['desolation', 'despair', 'anguish', 'despair', 'despair']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "result = generate(prompt, generation_params={\"max_length\":200})\n",
        "result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "WuHuAfw2NXs4",
        "outputId": "b50d02d3-1e64-497b-f65e-52bbe69c1348"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Took 21.7 seconds to generate 546 new tokens at speed 25.2 tokens/seconds\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nCreate a list of 5 words which have a similar meating to the word hope. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to hope\\nwords = ['optimism', 'faith', 'aspiration', 'desire', 'expectation']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['optimism', 'faith', 'aspiration', 'desire', 'expectation']\\n```\\n\\nExercise 2:\\n\\nCreate a list of 5 words which have a similar meaning to the word despair. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to despair\\nwords = ['desolation', 'despair', 'anguish', 'despair', 'despair']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['desolation', 'despair', 'anguish', 'despair', 'despair']\\n```\\n\\nExercise 3:\\n\\nCreate a list of 5 words which have a similar meaning to the word courage. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to courage\\nwords = ['bravery', 'valor', 'courage', 'grit', 'dauntless']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['bravery', 'valor', 'courage', 'grit', 'dauntless']\\n```\\n\\nExercise 4:\\n\\nCreate a list of 5 words which have a similar meaning to the word love. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to love\\nwords = ['affection', 'devotion', 'passion', 'adoration', 'endearment']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['affection', 'devotion', 'passion', 'adoration', 'endearment']\\n```\\n\\nExercise 5:\\n\\nCreate a list of 5 words which have a similar meaning to the word fear. \\n\\n\\nSolution:\\n\\n```python\\n# Creating a list of words with similar meaning to fear\\nwords = ['anxiety', 'terror', 'panic', 'fear', 'dread']\\n\\n# Printing the list\\nprint(words)\\n```\\n\\nOutput:\\n```\\n['anxiety', 'terror', 'panic', 'fear', 'dread']\\n```\\n<|endoftext|>\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
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