{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "%pip install huggingface_hub httpx tqdm --upgrade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pCPC0RUTOt7i",
        "outputId": "2dff92e3-a9ae-4ef8-e47b-24f754556a01"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting huggingface_hub\n",
            "  Downloading huggingface_hub-0.17.2-py3-none-any.whl (294 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.9/294.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting httpx\n",
            "  Downloading httpx-0.25.0-py3-none-any.whl (75 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.7/75.7 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting httpcore<0.19.0,>=0.18.0 (from httpx)\n",
            "  Downloading httpcore-0.18.0-py3-none-any.whl (76 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.0/76.0 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting h11<0.15,>=0.13 (from httpcore<0.19.0,>=0.18.0->httpx)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n",
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            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.19.0,>=0.18.0->httpx) (1.1.3)\n",
            "Installing collected packages: h11, huggingface_hub, httpcore, httpx\n",
            "Successfully installed h11-0.14.0 httpcore-0.18.0 httpx-0.25.0 huggingface_hub-0.17.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "borSZCgAOfOo"
      },
      "outputs": [],
      "source": [
        "import httpx\n",
        "from huggingface_hub import list_models, ModelFilter, hf_hub_url\n",
        "import pandas as pd\n",
        "from tqdm.contrib.concurrent import thread_map\n",
        "from huggingface_hub import model_info\n",
        "from tqdm.auto import tqdm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0cwY4nNOfOo",
        "outputId": "a65725ea-97a1-4cb4-de1d-5cf0876ca4ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6981"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "peft_models = list(iter(list_models(filter=ModelFilter(library=\"peft\"), full=True)))\n",
        "len(peft_models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6yfMtGYcOfOq",
        "outputId": "d59f125b-ae61-4aeb-9316-519543fba3c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RepoFile: {'blob_id': None, 'lfs': None, 'rfilename': 'adapter_config.json', 'size': None}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "peft_models[0].siblings[2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLEeIwZAOfOq",
        "outputId": "ed4d1041-46f7-473d-d53e-f7ffbf5d4248"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'base_model_name_or_path': 'facebook/opt-350m',\n",
              " 'bias': 'none',\n",
              " 'enable_lora': None,\n",
              " 'fan_in_fan_out': False,\n",
              " 'inference_mode': True,\n",
              " 'lora_alpha': 32,\n",
              " 'lora_dropout': 0.05,\n",
              " 'merge_weights': False,\n",
              " 'peft_type': 'LORA',\n",
              " 'r': 16,\n",
              " 'target_modules': ['q_proj', 'v_proj'],\n",
              " 'task_type': 'SEQ_CLS'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "httpx.get(hf_hub_url(peft_models[0].modelId,'adapter_config.json')).json()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "xMM2229VOfOq",
        "outputId": "889bb7ad-c90f-41e7-98de-ec56a700ac44"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'facebook/opt-350m'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "httpx.get(hf_hub_url(peft_models[0].modelId,'adapter_config.json')).json().get('base_model_name_or_path')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "OjKGmWMDOfOr"
      },
      "outputs": [],
      "source": [
        "def try_get_base_model(model):\n",
        "    model_id = model.modelId\n",
        "    downloads = model.downloads\n",
        "    author = model.author\n",
        "    try:\n",
        "        return model_id, httpx.get(hf_hub_url(model_id,'adapter_config.json')).json().get('base_model_name_or_path'), downloads, author\n",
        "    except Exception:\n",
        "        return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "9dc99b649eaa4e4eb68d1aa37651566e",
            "efdcc8fa8b0d417ab2d63f5cee541aed",
            "a9a4ce193d224638bc76f527ead85cbf",
            "9ba1d7f9e9014d58a98a9e0ac45c7cc9",
            "f408225ced644598981dcf25fae01591",
            "1ab7d6ccc0f54e779f368ea719b4785b",
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            "d64eb69ca9934364b164ed432bb71141",
            "37dfab2a0c5c43dcac7b2cd6960bfda5"
          ]
        },
        "id": "ovssytREOfOr",
        "outputId": "0c67429d-219c-4f58-9ae4-784a0accc1de"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/6981 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9dc99b649eaa4e4eb68d1aa37651566e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "results = thread_map(try_get_base_model, peft_models)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "IGxeikIsOfOs",
        "outputId": "f8aadaf6-fc5c-47db-83fb-a4a2510764ea"
      },
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        {
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              "                                               model_id  \\\n",
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              "1065     peft-internal-testing/tiny_T5ForSeq2SeqLM-lora   \n",
              "1063  peft-internal-testing/tiny_OPTForQuestionAnswe...   \n",
              "...                                                 ...   \n",
              "2569  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
              "2567  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
              "2566  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
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              "6980              AhmedBou/Falcon_7B_Science_Exam_QLoRA   \n",
              "\n",
              "                                             base_model  downloads  \\\n",
              "562   /home/ubuntu/.local/share/bentoml/models/pt-fa...   290304.0   \n",
              "914                               bigscience/bloom-560m    33069.0   \n",
              "1061     hf-internal-testing/tiny-random-OPTForCausalLM    17217.0   \n",
              "1065  trl-internal-testing/tiny-T5ForConditionalGene...    15197.0   \n",
              "1063     hf-internal-testing/tiny-random-OPTForCausalLM    15182.0   \n",
              "...                                                 ...        ...   \n",
              "2569                                               gpt2        0.0   \n",
              "2567                                               gpt2        0.0   \n",
              "2566                                               gpt2        0.0   \n",
              "2565                                               gpt2        0.0   \n",
              "6980                                   tiiuae/falcon-7b        0.0   \n",
              "\n",
              "                     author  \n",
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              "1061  peft-internal-testing  \n",
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              "...                     ...  \n",
              "2569              KingKazma  \n",
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              "2565              KingKazma  \n",
              "6980               AhmedBou  \n",
              "\n",
              "[6663 rows x 4 columns]"
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bde5173d-ba90-4b7e-a841-bb4023fb3d18\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bde5173d-ba90-4b7e-a841-bb4023fb3d18')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bde5173d-ba90-4b7e-a841-bb4023fb3d18 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df = pd.DataFrame(results, columns=['model_id','base_model','downloads','author']).dropna().sort_values('downloads',ascending=False)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63M7CSfKOfOs",
        "outputId": "f8af6b3b-5826-41b4-c444-9cdb88048f0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "gpt2                                 1069\n",
              "TinyPixel/Llama-2-7B-bf16-sharded     456\n",
              "t5-small                              344\n",
              "decapoda-research/llama-7b-hf         239\n",
              "google/flan-t5-large                  235\n",
              "                                     ... \n",
              "TheBloke/Llama-2-70B-Chat-fp16          1\n",
              "qqqube/spre4-lora2                      1\n",
              "qqqube/spre4-nw-lora2                   1\n",
              "qqqube/spre4-pft4                       1\n",
              "openai/clip-vit-base-patch32            1\n",
              "Name: base_model, Length: 470, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df['base_model'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "2Y4HhtV7OfOs",
        "outputId": "9e676501-b166-4340-8e97-d30c7168e955"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt = df['base_model'].value_counts().head(10).sort_values(ascending=True).plot(kind='barh')\n",
        "plt.figure.savefig('top_models.png')\n",
        "plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "cHglDQCJOfOt"
      },
      "outputs": [],
      "source": [
        "base_models = set(df['base_model'].to_list())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439,
          "referenced_widgets": [
            "03634aa14ea64dd89abe8377108574f3",
            "6235c2308ff34df28bdbbd3318f13e7d",
            "7b4c54d43bf04135a90542e386e6a761",
            "893e9a521c2940ba9ba10a83592ac748",
            "6974bbf9c3884957833463133314798b",
            "0fe173f545c341bb8386e7c6bf5fe097",
            "f0661c5eef1741b7ba9c647e6c101afc",
            "8219bab883e24bf9838f5124123e388c",
            "dcbb6c8b77de4683a17f532845f9d84d",
            "9378efb199964ce8b93db8155feed03e",
            "0e338245806c44d483d2c65cfbd4148e"
          ]
        },
        "id": "Q1Rk5TxoOfOt",
        "outputId": "8338a938-b50c-425e-c495-016e272abfb6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/470 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03634aa14ea64dd89abe8377108574f3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-750e411fc7a0>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbase_models\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[1;32m      3\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m         \u001b[0mmodel_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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      5\u001b[0m         \u001b[0mis_valid_hub_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\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/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    116\u001b[0m             \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmoothly_deprecate_use_auth_token\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhas_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhas_token\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_inner_fn\u001b[0m  \u001b[0;31m# type: ignore\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/huggingface_hub/hf_api.py\u001b[0m in \u001b[0;36mmodel_info\u001b[0;34m(self, repo_id, revision, timeout, securityStatus, files_metadata, token)\u001b[0m\n\u001b[1;32m   1695\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfiles_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1696\u001b[0m             \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"blobs\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1697\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\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   1698\u001b[0m         \u001b[0mhf_raise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1699\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\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/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"allow_redirects\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 602\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"GET\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    603\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    604\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    587\u001b[0m         }\n\u001b[1;32m    588\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 589\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\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    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    591\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\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/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 703\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    705\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\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/huggingface_hub/utils/_http.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, *args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;34m\"\"\"Catch any RequestException to append request id to the error message for debugging.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     64\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRequestException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0mrequest_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_AMZN_TRACE_ID\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/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m             resp = conn.urlopen(\n\u001b[0m\u001b[1;32m    487\u001b[0m                 \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\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/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001b[0m\n\u001b[1;32m    788\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    789\u001b[0m             \u001b[0;31m# Make the request on the HTTPConnection object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 790\u001b[0;31m             response = self._make_request(\n\u001b[0m\u001b[1;32m    791\u001b[0m                 \u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    792\u001b[0m                 \u001b[0mmethod\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/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001b[0m\n\u001b[1;32m    534\u001b[0m         \u001b[0;31m# Receive the response from the server\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 536\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\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[0m\u001b[1;32m    537\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    538\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\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/urllib3/connection.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m         \u001b[0;31m# Get the response from http.client.HTTPConnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 461\u001b[0;31m         \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\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[0m\u001b[1;32m    462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    463\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1373\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1375\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\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[0m\u001b[1;32m   1376\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1377\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\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/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    316\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\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[0m\u001b[1;32m    319\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    277\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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--> 279\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\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    280\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    703\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\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    706\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1272\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1273\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1274\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\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   1275\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1276\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m   1128\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1129\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1130\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\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   1131\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1132\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "is_valid_hub_id = []\n",
        "for model in tqdm(base_models):\n",
        "    try:\n",
        "        model_info(model)\n",
        "        is_valid_hub_id.append(model)\n",
        "    except Exception:\n",
        "        continue\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0NU5xtzOfOt",
        "outputId": "f6863100-1c64-4748-b095-18c7903af30c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "470"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "len(is_valid_hub_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "RWvgX-U8OfOt"
      },
      "outputs": [],
      "source": [
        "df = df[df.apply(lambda x: x['base_model'] in is_valid_hub_id, axis=1)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "GSQ4WPMWOfOt",
        "outputId": "f9a9642c-780c-45b5-95a2-4b5b2fd462fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               model_id  \\\n",
              "914                   artek0chumak/bloom-560m-safe-peft   \n",
              "1061     peft-internal-testing/tiny-OPTForCausalLM-lora   \n",
              "1065     peft-internal-testing/tiny_T5ForSeq2SeqLM-lora   \n",
              "1063  peft-internal-testing/tiny_OPTForQuestionAnswe...   \n",
              "1066  peft-internal-testing/tiny_OPTForSequenceClass...   \n",
              "...                                                 ...   \n",
              "2569  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
              "2567  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
              "2566  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
              "2565  KingKazma/cnn_dailymail_gpt2_p_tuning_500_10_3...   \n",
              "6980              AhmedBou/Falcon_7B_Science_Exam_QLoRA   \n",
              "\n",
              "                                             base_model  downloads  \\\n",
              "914                               bigscience/bloom-560m    33069.0   \n",
              "1061     hf-internal-testing/tiny-random-OPTForCausalLM    17217.0   \n",
              "1065  trl-internal-testing/tiny-T5ForConditionalGene...    15197.0   \n",
              "1063     hf-internal-testing/tiny-random-OPTForCausalLM    15182.0   \n",
              "1066     hf-internal-testing/tiny-random-OPTForCausalLM    15178.0   \n",
              "...                                                 ...        ...   \n",
              "2569                                               gpt2        0.0   \n",
              "2567                                               gpt2        0.0   \n",
              "2566                                               gpt2        0.0   \n",
              "2565                                               gpt2        0.0   \n",
              "6980                                   tiiuae/falcon-7b        0.0   \n",
              "\n",
              "                     author  \n",
              "914            artek0chumak  \n",
              "1061  peft-internal-testing  \n",
              "1065  peft-internal-testing  \n",
              "1063  peft-internal-testing  \n",
              "1066  peft-internal-testing  \n",
              "...                     ...  \n",
              "2569              KingKazma  \n",
              "2567              KingKazma  \n",
              "2566              KingKazma  \n",
              "2565              KingKazma  \n",
              "6980               AhmedBou  \n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "n2qZClsEOfOu",
        "outputId": "bf95c832-af06-4cb0-e351-b1d73854005b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df['base_model'].value_counts().head(10).sort_values(ascending=True).plot(kind='barh')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "jU8Rg0x9OfOu",
        "outputId": "016697c4-c282-4d54-8c9f-09c029129991"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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        "plt = df.groupby('base_model').sum(numeric_only=True).sort_values('downloads',ascending=False).head(10).sort_values('downloads',ascending=True).plot(kind='barh')\n",
        "plt.figure.savefig('top_model_children_downloads.png')"
      ]
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