{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lIYdn1woOS1n",
        "outputId": "9df704ad-c044-4172-9f2c-7e902fda9abf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting git+https://github.com/huggingface/huggingface_hub\n",
            "  Cloning https://github.com/huggingface/huggingface_hub to /tmp/pip-req-build-epzdr933\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/huggingface_hub /tmp/pip-req-build-epzdr933\n",
            "  Resolved https://github.com/huggingface/huggingface_hub to commit f7529325e0739a0c818f84d50b9642b30918407f\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 huggingface-hub==0.18.0.dev0) (3.12.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub==0.18.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub==0.18.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub==0.18.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub==0.18.0.dev0) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub==0.18.0.dev0) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub==0.18.0.dev0) (23.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub==0.18.0.dev0) (3.3.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub==0.18.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub==0.18.0.dev0) (2.0.6)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub==0.18.0.dev0) (2023.7.22)\n",
            "Building wheels for collected packages: huggingface-hub\n",
            "  Building wheel for huggingface-hub (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for huggingface-hub: filename=huggingface_hub-0.18.0.dev0-py3-none-any.whl size=300994 sha256=a318ecf0e26f2cb86afe747d4918d8028e1958d535d2ee34a92a1dd8b67016bb\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-zzrfj0sm/wheels/81/77/10/4ea0848421de7e11b030d8127ca1139b1e0e254f714938175f\n",
            "Successfully built huggingface-hub\n",
            "Installing collected packages: huggingface-hub\n",
            "Successfully installed huggingface-hub-0.18.0.dev0\n"
          ]
        }
      ],
      "source": [
        "%pip install git+https://github.com/huggingface/huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import list_models, model_info\n",
        "from tqdm.contrib.concurrent import thread_map\n",
        "from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError\n",
        "from huggingface_hub import login\n",
        "from huggingface_hub import HfApi"
      ],
      "metadata": {
        "id": "YSYeCyTg-8Ls"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "login()"
      ],
      "metadata": {
        "id": "O4Fd-N9GF51S",
        "outputId": "8524e8e7-f459-4470-c4fc-d432e22d9657",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "4e31f722accb4fd790c29ed1729d925c"
          ]
        }
      },
      "execution_count": 3,
      "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": "d5c2b4015d474ec28970e67606c675fd"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "api = HfApi()"
      ],
      "metadata": {
        "id": "EYz_e-0WF8z7"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def check_has_readme(model):\n",
        "    if any([file.rfilename for file in model.siblings if file.rfilename == \"README.md\"]):\n",
        "        return True\n",
        "    return False"
      ],
      "metadata": {
        "id": "HQsx6NGLYfDu"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models = api.list_models(limit=None,full=True, cardData=True)\n",
        "models = (model for model in models if model.downloads >20) # must have some downloads\n",
        "models = (model for model in models if model.likes >3) # must have at least 1 like\n",
        "models = (model for model in models if model.likes >= 5 or model.downloads >= 200) # must have either many downloads or many likes\n",
        "models = (model for model in models if check_has_readme(model))"
      ],
      "metadata": {
        "id": "imkwBNqf_Fa_"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def yield_models_with_files(models):\n",
        "    for model in models:\n",
        "        if any([file.rfilename for file in model.siblings if file.rfilename in {\"model.safetensors\", \"pytorch_model.bin\"}]):\n",
        "            yield model\n",
        "        # if any([file.rfilename for file in model.siblings if file.rfilename.split(\".\")[-1] in {\"onnx\"}]):\n",
        "        #     yield model"
      ],
      "metadata": {
        "id": "VCoUAHYuABNe"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "models_with_files = list(yield_models_with_files(models))"
      ],
      "metadata": {
        "id": "srkgQMHE_Qdn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def bytes_to_mb(bytes_value):\n",
        "    return bytes_value / (2**20)"
      ],
      "metadata": {
        "id": "jV7O9XlAB58s"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def yield_candidate_files(files):\n",
        "    for file in files:\n",
        "        if file.rfilename in {\"model.safetensors\", \"pytorch_model.bin\"}:\n",
        "            yield file\n",
        "        # if file.rfilename.split(\".\")[-1] in {\"onnx\"}:\n",
        "        #     yield file"
      ],
      "metadata": {
        "id": "QodpLDluLhZ6"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list(yield_candidate_files(model_info('TaylorAI/gte-tiny').siblings))"
      ],
      "metadata": {
        "id": "hAOet9LwOeOC",
        "outputId": "b19ec541-26b6-44c5-963a-75781456ad74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[RepoFile: {'blob_id': None, 'lfs': None, 'rfilename': 'model.safetensors', 'size': None},\n",
              " RepoFile: {'blob_id': None, 'lfs': None, 'rfilename': 'pytorch_model.bin', 'size': None}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_info('TaylorAI/gte-tiny').downloads"
      ],
      "metadata": {
        "id": "mjiP33LynL1b",
        "outputId": "5c6723d3-b730-4b01-f205-e3851e1fa94a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def check_model_file_below_threshold(model, threshold=50.0):\n",
        "    try:\n",
        "        model_id = model.id\n",
        "        files = api.model_info(model_id, files_metadata=True).siblings\n",
        "        files = yield_candidate_files(files)\n",
        "        sizes = (file.size for file in files)\n",
        "        for size in sizes:\n",
        "            mb = bytes_to_mb(size)\n",
        "            if round(mb)< threshold:\n",
        "                return model_id, mb, model.likes, model.downloads\n",
        "        return False\n",
        "    except GatedRepoError:\n",
        "        return False\n",
        "    except RepositoryNotFoundError:\n",
        "        return False"
      ],
      "metadata": {
        "id": "wX6yElKqA4a8"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "check_model_file_below_threshold(model_info('TaylorAI/gte-tiny'))"
      ],
      "metadata": {
        "id": "7A0DxQW0MWbl",
        "outputId": "fe514b69-ff2a-43f7-fce2-5a42f949a04b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('TaylorAI/gte-tiny', 43.351722717285156, 10, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = thread_map(check_model_file_below_threshold, models_with_files)"
      ],
      "metadata": {
        "id": "LsNKKz9DDfqv",
        "outputId": "e7fac352-8762-4eab-be4b-9d8595c02ffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3126 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = [r for r in results if r]"
      ],
      "metadata": {
        "id": "bv0-ING4EAT-"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(results)"
      ],
      "metadata": {
        "id": "4_IyR44BFD0u",
        "outputId": "131a1669-ea19-493b-bd05-f109cf3321ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results[:10]"
      ],
      "metadata": {
        "id": "TT3v-9shKmLz",
        "outputId": "2d881ec7-3745-4a1a-fba9-eb4d23524d7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('albert-base-v2', 45.17831230163574, 63, 6608487),\n",
              " ('JorisCos/DCCRNet_Libri1Mix_enhsingle_16k', 15.648945808410645, 13, 1214),\n",
              " ('JorisCos/DCUNet_Libri1Mix_enhsingle_16k', 37.35937023162842, 4, 594),\n",
              " ('WinKawaks/vit-tiny-patch16-224', 21.8319149017334, 5, 4619),\n",
              " ('ckiplab/albert-base-chinese-ner', 38.21315860748291, 7, 1825),\n",
              " ('ckiplab/albert-base-chinese', 38.4575080871582, 6, 433),\n",
              " ('ckiplab/albert-tiny-chinese', 15.443592071533203, 6, 713),\n",
              " ('clue/albert_chinese_tiny', 15.807161331176758, 15, 190),\n",
              " ('cointegrated/rubert-tiny-sentiment-balanced', 44.96676063537598, 7, 1408),\n",
              " ('cointegrated/rubert-tiny-toxicity', 44.96914863586426, 21, 135108)]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import create_collection, add_collection_item"
      ],
      "metadata": {
        "id": "I8mYKrQnmnQy"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "description = \"\"\"Models where the size of the model file (model.safetensors or pytorch_model.bin) < 50mb\"\"\""
      ],
      "metadata": {
        "id": "jBoq23cDwRoH"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "collection = create_collection(\"smol models\",namespace='librarian-bots',description=description)"
      ],
      "metadata": {
        "id": "_wM_uNlBwAs3"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for row in sorted(results, key=lambda x: x[-2], reverse=True):\n",
        "    add_collection_item(collection.slug, row[0],item_type='model', note=f\"Model file is {round(row[1])}mb\")\n"
      ],
      "metadata": {
        "id": "yYhLfPQZwN8S"
      },
      "execution_count": 32,
      "outputs": []
    }
  ],
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      "display_name": "Python 3",
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