LLM_OS.ipynb

{
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        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hilaw/Python_NLP_Tutorial/blob/master/LLM_OS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3be2c3d7-e637-4ca0-b823-1442ce1ef014",
      "metadata": {
        "id": "3be2c3d7-e637-4ca0-b823-1442ce1ef014"
      },
      "source": [
        "# Running an LLM on your own laptop\n",
        "In this notebook, we're going to learn how to run a Hugging Face LLM on our own machine."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0c88379d-de11-4f6b-b3b2-dd1c86ec1f2d",
      "metadata": {
        "id": "0c88379d-de11-4f6b-b3b2-dd1c86ec1f2d"
      },
      "source": [
        "## Download the LLM\n",
        "We're going to write some code to manually download the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "5eba955c-10f9-435a-8f5c-a4f3148214f6",
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        "id": "5eba955c-10f9-435a-8f5c-a4f3148214f6"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from huggingface_hub import hf_hub_download"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "e1769883-5d03-45a6-89a4-53d97a2b31cb",
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          "base_uri": "https://localhost:8080/",
          "height": 315
        },
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        "outputId": "4b28a536-7f38-4474-959f-579c55498be0"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "NotebookAccessError",
          "evalue": "Notebook does not have access to secret llm",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNotebookAccessError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-edfd1d258bc5>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0muserdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhf_api_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0muserdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'llm'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mHUGGING_FACE_API_KEY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhf_api_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/userdata.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(key)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[0;32mraise\u001b[0m \u001b[0mSecretNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'access'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mNotebookAccessError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     71\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'payload'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNotebookAccessError\u001b[0m: Notebook does not have access to secret llm"
          ]
        }
      ],
      "source": [
        "from google.colab import userdata\n",
        "hf_api_key = userdata.get('llm')\n",
        "\n",
        "\n",
        "HUGGING_FACE_API_KEY = os.environ.get(hf_api_key)"
      ]
    },
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        },
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      },
      "outputs": [],
      "source": [
        "model_id = \"lmsys/fastchat-t5-3b-v1.0\"\n",
        "filenames = [\n",
        "        \"pytorch_model.bin\", \"added_tokens.json\", \"config.json\", \"generation_config.json\",\n",
        "        \"special_tokens_map.json\", \"spiece.model\", \"tokenizer_config.json\"\n",
        "]"
      ]
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      "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": [
              "pytorch_model.bin:   0%|          | 0.00/6.71G [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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            }
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          "metadata": {}
        },
        {
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          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/pytorch_model.bin\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "added_tokens.json:   0%|          | 0.00/150 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "532cf4c0791c4f46a4ed5e1543c5bea9"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/added_tokens.json\n"
          ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.52k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "152128b0a2ab40d1913e5460c51ed95c"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/config.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/142 [00:00<?, ?B/s]"
            ],
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              "version_minor": 0,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/generation_config.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d4a983f451d49f5aad3604cbf330107"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/special_tokens_map.json\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/spiece.model\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/2.40k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1a17af67fa24874ad470a18d0aa041b"
            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/root/.cache/huggingface/hub/models--lmsys--fastchat-t5-3b-v1.0/snapshots/0b1da230a891854102d749b93f7ddf1f18a81024/tokenizer_config.json\n"
          ]
        }
      ],
      "source": [
        "for filename in filenames:\n",
        "        downloaded_model_path = hf_hub_download(\n",
        "                    repo_id=model_id,\n",
        "                    filename=filename,\n",
        "                    token=HUGGING_FACE_API_KEY\n",
        "        )\n",
        "        print(downloaded_model_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d5b4ad22-2e7d-40d9-978b-b730bd06b141",
      "metadata": {
        "id": "d5b4ad22-2e7d-40d9-978b-b730bd06b141"
      },
      "source": [
        "## Run the LLM\n",
        "Now let's try running the model. But before we do that, let's disable the Wi-Fi."
      ]
    },
    {
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        "outputId": "43f7593d-c851-47c4-d5ff-948dba173a01"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "You have Network Connectivity (IP Address: 192.168.86.249)\n",
            "WiFi disabled.\n",
            "No Network Connectivity (IP Address: 127.0.0.1)\n"
          ]
        }
      ],
      "source": [
        "from utils import check_connectivity, toggle_wifi\n",
        "import time\n",
        "\n",
        "print(check_connectivity())\n",
        "toggle_wifi(\"off\")\n",
        "time.sleep(0.5)\n",
        "print(check_connectivity())"
      ]
    },
    {
      "cell_type": "code",
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      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "'(ReadTimeoutError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Read timed out. (read timeout=10)\"), '(Request ID: f38c0ab3-254b-48dd-9ca8-8b65f92bdb17)')' thrown while requesting HEAD https://huggingface.co/lmsys/fastchat-t5-3b-v1.0/resolve/main/tokenizer_config.json\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id, legacy=False)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id)\n",
        "\n",
        "pipeline = pipeline(\"text2text-generation\", model=model, device=-1, tokenizer=tokenizer, max_length=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93138286-8497-4730-9da4-3f52390bd380",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-31T21:49:52.818571Z",
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          "iopub.status.idle": "2023-07-31T21:50:03.906446Z",
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          "shell.execute_reply.started": "2023-07-31T21:49:52.818537Z"
        },
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        "outputId": "31f5fba5-54be-4c91-ba82-c15a0cbb35b7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'generated_text': '<pad> Apache Kafka is a popular open source message broker that is used for streaming and aggregating data from multiple sources. Some of its competitors include Apache Spark, Apache Storm, Apache Flume, and Apache Flink.'}]"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline(\"What are competitors to Apache Kafka?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ca79ce5d-2e44-446c-a899-d9ecc4b77caa",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2023-07-31T21:50:28.502177Z",
          "iopub.status.busy": "2023-07-31T21:50:28.501810Z",
          "iopub.status.idle": "2023-07-31T21:50:33.145049Z",
          "shell.execute_reply": "2023-07-31T21:50:33.144699Z",
          "shell.execute_reply.started": "2023-07-31T21:50:28.502152Z"
        },
        "id": "ca79ce5d-2e44-446c-a899-d9ecc4b77caa",
        "outputId": "2c1788dc-a9e7-4c1e-d442-bffe064968b7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[{'generated_text': '<pad> No,  I  do  not  have  a  sister.\\n'}]"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pipeline(\"\"\"My name is Mark.\n",
        "I have brothers called David and John and my best friend is Michael.\n",
        "Using only the context above. Do you know if I have a sister?\n",
        "\"\"\")"
      ]
    }
  ],
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