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"cells": [
{
"cell_type": "markdown",
"source": [
"<h2 align=center> OLMoE-1B-7B </h2>"
],
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},
{
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"source": [
"The transformer library requires special install steps because the code that supports the OMOE models was only introduced in this [PR](https://github.com/huggingface/transformers/pull/32406).\n",
"This code has not been updated on PYPI so we have to install it from source."
],
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},
{
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"!git branch -m main"
],
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},
"id": "2AziwR7vq2bq",
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},
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"text": [
"\u001b[33mhint: Using 'master' as the name for the initial branch. This default branch name\u001b[m\n",
"\u001b[33mhint: is subject to change. To configure the initial branch name to use in all\u001b[m\n",
"\u001b[33mhint: of your new repositories, which will suppress this warning, call:\u001b[m\n",
"\u001b[33mhint: \u001b[m\n",
"\u001b[33mhint: \tgit config --global init.defaultBranch <name>\u001b[m\n",
"\u001b[33mhint: \u001b[m\n",
"\u001b[33mhint: Names commonly chosen instead of 'master' are 'main', 'trunk' and\u001b[m\n",
"\u001b[33mhint: 'development'. The just-created branch can be renamed via this command:\u001b[m\n",
"\u001b[33mhint: \u001b[m\n",
"\u001b[33mhint: \tgit branch -m <name>\u001b[m\n",
"Initialized empty Git repository in /content/.git/\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!export PR_NUMBER=32406"
],
"metadata": {
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},
"id": "bqhf8TGhxvB9",
"outputId": "101d77d0-5957-4534-b9a6-f3c794af86f2"
},
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!git remote add origin https://github.com/huggingface/transformers.git"
],
"metadata": {
"id": "PL3UBh06qo_C"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7bitxdKMptwl",
"outputId": "9c5016c2-b7ca-4477-e1f4-c896e5ca6f5a",
"collapsed": true
},
"outputs": [
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"output_type": "stream",
"name": "stdout",
"text": [
"remote: Enumerating objects: 188166, done.\u001b[K\n",
"remote: Counting objects: 2% (1/47)\u001b[K\rremote: Counting objects: 4% (2/47)\u001b[K\rremote: Counting objects: 6% (3/47)\u001b[K\rremote: Counting objects: 8% (4/47)\u001b[K\rremote: Counting objects: 10% (5/47)\u001b[K\rremote: Counting objects: 12% (6/47)\u001b[K\rremote: Counting objects: 14% (7/47)\u001b[K\rremote: Counting objects: 17% (8/47)\u001b[K\rremote: Counting objects: 19% (9/47)\u001b[K\rremote: Counting objects: 21% (10/47)\u001b[K\rremote: Counting objects: 23% (11/47)\u001b[K\rremote: Counting objects: 25% (12/47)\u001b[K\rremote: Counting objects: 27% (13/47)\u001b[K\rremote: Counting objects: 29% (14/47)\u001b[K\rremote: Counting objects: 31% (15/47)\u001b[K\rremote: Counting objects: 34% (16/47)\u001b[K\rremote: Counting objects: 36% (17/47)\u001b[K\rremote: Counting objects: 38% (18/47)\u001b[K\rremote: Counting objects: 40% (19/47)\u001b[K\rremote: Counting objects: 42% (20/47)\u001b[K\rremote: Counting objects: 44% (21/47)\u001b[K\rremote: Counting objects: 46% (22/47)\u001b[K\rremote: Counting objects: 48% (23/47)\u001b[K\rremote: Counting objects: 51% (24/47)\u001b[K\rremote: Counting objects: 53% (25/47)\u001b[K\rremote: Counting objects: 55% (26/47)\u001b[K\rremote: Counting objects: 57% (27/47)\u001b[K\rremote: Counting objects: 59% (28/47)\u001b[K\rremote: Counting objects: 61% (29/47)\u001b[K\rremote: Counting objects: 63% (30/47)\u001b[K\rremote: Counting objects: 65% (31/47)\u001b[K\rremote: Counting objects: 68% (32/47)\u001b[K\rremote: Counting objects: 70% (33/47)\u001b[K\rremote: Counting objects: 72% (34/47)\u001b[K\rremote: Counting objects: 74% (35/47)\u001b[K\rremote: Counting objects: 76% (36/47)\u001b[K\rremote: Counting objects: 78% (37/47)\u001b[K\rremote: Counting objects: 80% (38/47)\u001b[K\rremote: Counting objects: 82% (39/47)\u001b[K\rremote: Counting objects: 85% (40/47)\u001b[K\rremote: Counting objects: 87% (41/47)\u001b[K\rremote: Counting objects: 89% (42/47)\u001b[K\rremote: Counting objects: 91% (43/47)\u001b[K\rremote: Counting objects: 93% (44/47)\u001b[K\rremote: Counting objects: 95% (45/47)\u001b[K\rremote: Counting objects: 97% (46/47)\u001b[K\rremote: Counting objects: 100% (47/47)\u001b[K\rremote: Counting objects: 100% (47/47), done.\u001b[K\n",
"remote: Compressing objects: 100% (46/46), done.\u001b[K\n",
"remote: Total 188166 (delta 1), reused 47 (delta 1), pack-reused 188119 (from 1)\u001b[K\n",
"Receiving objects: 100% (188166/188166), 214.64 MiB | 20.89 MiB/s, done.\n",
"Resolving deltas: 100% (134665/134665), done.\n",
"From https://github.com/huggingface/transformers\n",
" * [new ref] refs/pull/32406/head -> pr-32406\n",
" * [new tag] 0.1.2 -> 0.1.2\n",
" * [new tag] 0.5.0 -> 0.5.0\n",
" * [new tag] 1.0 -> 1.0\n",
" * [new tag] 1.1.0 -> 1.1.0\n",
" * [new tag] 1.2.0 -> 1.2.0\n",
" * [new tag] 3.0.1 -> 3.0.1\n",
" * [new tag] 4.3.0.rc1 -> 4.3.0.rc1\n",
" * [new tag] v0.1.2 -> v0.1.2\n",
" * [new tag] v0.2.0 -> v0.2.0\n",
" * [new tag] v0.3.0 -> v0.3.0\n",
" * [new tag] v0.4.0 -> v0.4.0\n",
" * [new tag] v0.5.0 -> v0.5.0\n",
" * [new tag] v0.5.1 -> v0.5.1\n",
" * [new tag] v0.6.0 -> v0.6.0\n",
" * [new tag] v0.6.1 -> v0.6.1\n",
" * [new tag] v0.6.2 -> v0.6.2\n",
" * [new tag] v1.0.0 -> v1.0.0\n",
" * [new tag] v2.0.0 -> v2.0.0\n",
" * [new tag] v2.1.0 -> v2.1.0\n",
" * [new tag] v2.1.1 -> v2.1.1\n",
" * [new tag] v2.10.0 -> v2.10.0\n",
" * [new tag] v2.11.0 -> v2.11.0\n",
" * [new tag] v2.2.0 -> v2.2.0\n",
" * [new tag] v2.2.1 -> v2.2.1\n",
" * [new tag] v2.2.2 -> v2.2.2\n",
" * [new tag] v2.3.0 -> v2.3.0\n",
" * [new tag] v2.4.0 -> v2.4.0\n",
" * [new tag] v2.4.1 -> v2.4.1\n",
" * [new tag] v2.5.0 -> v2.5.0\n",
" * [new tag] v2.5.1 -> v2.5.1\n",
" * [new tag] v2.6.0 -> v2.6.0\n",
" * [new tag] v2.7.0 -> v2.7.0\n",
" * [new tag] v2.8.0 -> v2.8.0\n",
" * [new tag] v2.9.0 -> v2.9.0\n",
" * [new tag] v2.9.1 -> v2.9.1\n",
" * [new tag] v3.0.0 -> v3.0.0\n",
" * [new tag] v3.0.1 -> v3.0.1\n",
" * [new tag] v3.0.2 -> v3.0.2\n",
" * [new tag] v3.1.0 -> v3.1.0\n",
" * [new tag] v3.2.0 -> v3.2.0\n",
" * [new tag] v3.3.0 -> v3.3.0\n",
" * [new tag] v3.3.1 -> v3.3.1\n",
" * [new tag] v3.4.0 -> v3.4.0\n",
" * [new tag] v3.5.0 -> v3.5.0\n",
" * [new tag] v4.0.0-rc-1 -> v4.0.0-rc-1\n",
" * [new tag] v4.1.0 -> v4.1.0\n",
" * [new tag] v4.1.1 -> v4.1.1\n",
" * [new tag] v4.10.0 -> v4.10.0\n",
" * [new tag] v4.11.0 -> v4.11.0\n",
" * [new tag] v4.12.0 -> v4.12.0\n",
" * [new tag] v4.13.0 -> v4.13.0\n",
" * [new tag] v4.14.0 -> v4.14.0\n",
" * [new tag] v4.15.0 -> v4.15.0\n",
" * [new tag] v4.16.0 -> v4.16.0\n",
" * [new tag] v4.2.0 -> v4.2.0\n",
" * [new tag] v4.3.0.rc1 -> v4.3.0.rc1\n",
" * [new tag] v4.33.1 -> v4.33.1\n",
" * [new tag] v4.4.0 -> v4.4.0\n",
" * [new tag] v4.5.0 -> v4.5.0\n",
" * [new tag] v4.6.0 -> v4.6.0\n",
" * [new tag] v4.7.0 -> v4.7.0\n",
" * [new tag] v4.8.0 -> v4.8.0\n",
" * [new tag] v4.9.0 -> v4.9.0\n"
]
}
],
"source": [
"!git fetch origin pull/32406/head:pr-32406"
]
},
{
"cell_type": "code",
"source": [
"!git checkout pr-32406"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "_s3Mt90Hqbbd",
"outputId": "b8ac5e14-c28c-41f4-ba61-8e936497b125"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Updating files: 76% (3390/4452)\rUpdating files: 77% (3429/4452)\rUpdating files: 78% (3473/4452)\rUpdating files: 79% (3518/4452)\rUpdating files: 80% (3562/4452)\rUpdating files: 81% (3607/4452)\rUpdating files: 82% (3651/4452)\rUpdating files: 83% (3696/4452)\rUpdating files: 84% (3740/4452)\rUpdating files: 85% (3785/4452)\rUpdating files: 86% (3829/4452)\rUpdating files: 87% (3874/4452)\rUpdating files: 88% (3918/4452)\rUpdating files: 89% (3963/4452)\rUpdating files: 90% (4007/4452)\rUpdating files: 91% (4052/4452)\rUpdating files: 92% (4096/4452)\rUpdating files: 93% (4141/4452)\rUpdating files: 94% (4185/4452)\rUpdating files: 95% (4230/4452)\rUpdating files: 96% (4274/4452)\rUpdating files: 97% (4319/4452)\rUpdating files: 98% (4363/4452)\rUpdating files: 99% (4408/4452)\rUpdating files: 100% (4452/4452)\rUpdating files: 100% (4452/4452), done.\n",
"Switched to branch 'pr-32406'\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -e .[all]"
],
"metadata": {
"id": "cq0YWuyxrQbf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"collapsed": true,
"outputId": "9c6ea2e5-74f5-4f61-9043-215b7de2572e"
},
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Obtaining file:///content\n",
" Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[?25hBuilding wheels for collected packages: transformers, jax, kenlm, fire, pylatexenc\n",
" Building editable for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for transformers: filename=transformers-4.45.0.dev0-0.editable-py3-none-any.whl size=17235 sha256=47f8d7fd33fbf68a16dcdf9eb478de0330868e96b41dddcaa2e90ce820af3d0c\n",
" Stored in directory: /tmp/pip-ephem-wheel-cache-4a6k3c9o/wheels/e8/d3/96/0e8c7135806cbda4db28d12fc8d710e5e4f66ced1411163e67\n",
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" Stored in directory: /root/.cache/pip/wheels/f3/7a/25/f297f69029b5e4064e4736a0c4b3996a44cc27781c120bcb99\n",
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" Created wheel for kenlm: filename=kenlm-0.2.0-cp310-cp310-linux_x86_64.whl size=3184429 sha256=13f3f18abd6e36cd73d41e00a8f62353ba919d0dc6283b26d233ce3d7a001263\n",
" Stored in directory: /root/.cache/pip/wheels/fd/80/e0/18f4148e863fb137bd87e21ee2bf423b81b3ed6989dab95135\n",
" Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for fire: filename=fire-0.6.0-py2.py3-none-any.whl size=117030 sha256=663b80270e4bec0b4b2a2170c8587b95783f5bb0683d5e2d919836b362b8e838\n",
" Stored in directory: /root/.cache/pip/wheels/d6/6d/5d/5b73fa0f46d01a793713f8859201361e9e581ced8c75e5c6a3\n",
" Building wheel for pylatexenc (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pylatexenc: filename=pylatexenc-2.10-py3-none-any.whl size=136817 sha256=ac148679a22a494d8e13b2f1d3c8edf4753a07b4a509bdc91ede1fac56069ade\n",
" Stored in directory: /root/.cache/pip/wheels/d3/31/8b/e09b0386afd80cfc556c00408c9aeea5c35c4d484a9c762fd5\n",
"Successfully built transformers jax kenlm fire pylatexenc\n",
"Installing collected packages: rfc3986, pypng, pylatexenc, pygtrie, language-tags, kenlm, dlinfo, dash-table, dash-html-components, dash-core-components, av, wrapt, urllib3, tensorflow-estimator, tensorboardX, smmap, scipy, retrying, pynvml, Pillow, onnx, ml-dtypes, Mako, keras, isodate, hypothesis, fire, decord, colorlog, colorama, bibtexparser, backoff, APScheduler, rdflib, pyctcdecode, onnxconverter-common, jaxlib, jax, gitdb, clldutils, alembic, tf2onnx, orbax-checkpoint, optuna, keras-core, GitPython, dash, chex, timm, sigopt, ray, optax, dash-bootstrap-components, csvw, transformers, tensorboard, segments, flax, codecarbon, tensorflow, phonemizer, tf-keras, tensorflow-text, keras-nlp\n",
" Attempting uninstall: wrapt\n",
" Found existing installation: wrapt 1.16.0\n",
" Uninstalling wrapt-1.16.0:\n",
" Successfully uninstalled wrapt-1.16.0\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 2.0.7\n",
" Uninstalling urllib3-2.0.7:\n",
" Successfully uninstalled urllib3-2.0.7\n",
" Attempting uninstall: scipy\n",
" Found existing installation: scipy 1.13.1\n",
" Uninstalling scipy-1.13.1:\n",
" Successfully uninstalled scipy-1.13.1\n",
" Attempting uninstall: Pillow\n",
" Found existing installation: Pillow 9.4.0\n",
" Uninstalling Pillow-9.4.0:\n",
" Successfully uninstalled Pillow-9.4.0\n",
" Attempting uninstall: ml-dtypes\n",
" Found existing installation: ml-dtypes 0.4.0\n",
" Uninstalling ml-dtypes-0.4.0:\n",
" Successfully uninstalled ml-dtypes-0.4.0\n",
" Attempting uninstall: keras\n",
" Found existing installation: keras 3.4.1\n",
" Uninstalling keras-3.4.1:\n",
" Successfully uninstalled keras-3.4.1\n",
" Attempting uninstall: jaxlib\n",
" Found existing installation: jaxlib 0.4.26+cuda12.cudnn89\n",
" Uninstalling jaxlib-0.4.26+cuda12.cudnn89:\n",
" Successfully uninstalled jaxlib-0.4.26+cuda12.cudnn89\n",
" Attempting uninstall: jax\n",
" Found existing installation: jax 0.4.26\n",
" Uninstalling jax-0.4.26:\n",
" Successfully uninstalled jax-0.4.26\n",
" Attempting uninstall: orbax-checkpoint\n",
" Found existing installation: orbax-checkpoint 0.6.3\n",
" Uninstalling orbax-checkpoint-0.6.3:\n",
" Successfully uninstalled orbax-checkpoint-0.6.3\n",
" Attempting uninstall: chex\n",
" Found existing installation: chex 0.1.86\n",
" Uninstalling chex-0.1.86:\n",
" Successfully uninstalled chex-0.1.86\n",
" Attempting uninstall: optax\n",
" Found existing installation: optax 0.2.2\n",
" Uninstalling optax-0.2.2:\n",
" Successfully uninstalled optax-0.2.2\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.44.2\n",
" Uninstalling transformers-4.44.2:\n",
" Successfully uninstalled transformers-4.44.2\n",
" Attempting uninstall: tensorboard\n",
" Found existing installation: tensorboard 2.17.0\n",
" Uninstalling tensorboard-2.17.0:\n",
" Successfully uninstalled tensorboard-2.17.0\n",
" Attempting uninstall: flax\n",
" Found existing installation: flax 0.8.4\n",
" Uninstalling flax-0.8.4:\n",
" Successfully uninstalled flax-0.8.4\n",
" Attempting uninstall: tensorflow\n",
" Found existing installation: tensorflow 2.17.0\n",
" Uninstalling tensorflow-2.17.0:\n",
" Successfully uninstalled tensorflow-2.17.0\n",
" Attempting uninstall: tf-keras\n",
" Found existing installation: tf_keras 2.17.0\n",
" Uninstalling tf_keras-2.17.0:\n",
" Successfully uninstalled tf_keras-2.17.0\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"osqp 0.6.7.post0 requires scipy!=1.12.0,>=0.13.2, but you have scipy 1.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed APScheduler-3.10.4 GitPython-3.1.43 Mako-1.3.5 Pillow-10.4.0 alembic-1.13.2 av-9.2.0 backoff-1.11.1 bibtexparser-2.0.0b7 chex-0.1.82 clldutils-3.22.2 codecarbon-1.2.0 colorama-0.4.6 colorlog-6.8.2 csvw-3.3.1 dash-2.18.1 dash-bootstrap-components-1.6.0 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 decord-0.6.0 dlinfo-1.2.1 fire-0.6.0 flax-0.7.0 gitdb-4.0.11 hypothesis-6.112.1 isodate-0.6.1 jax-0.4.13 jaxlib-0.4.13 kenlm-0.2.0 keras-2.15.0 keras-core-0.1.7 keras-nlp-0.12.1 language-tags-1.2.0 ml-dtypes-0.3.2 onnx-1.16.2 onnxconverter-common-1.13.0 optax-0.1.4 optuna-4.0.0 orbax-checkpoint-0.5.16 phonemizer-3.3.0 pyctcdecode-0.5.0 pygtrie-2.5.0 pylatexenc-2.10 pynvml-11.5.3 pypng-0.20220715.0 ray-2.35.0 rdflib-7.0.0 retrying-1.3.4 rfc3986-1.5.0 scipy-1.12.0 segments-2.2.1 sigopt-8.8.3 smmap-5.0.1 tensorboard-2.15.2 tensorboardX-2.6.2.2 tensorflow-2.15.1 tensorflow-estimator-2.15.0 tensorflow-text-2.15.0 tf-keras-2.15.1 tf2onnx-1.16.1 timm-0.9.16 transformers-4.45.0.dev0 urllib3-1.26.20 wrapt-1.14.1\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"PIL"
]
},
"id": "13f986199d30470480155a0a26153ae8"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import OlmoeForCausalLM, AutoTokenizer"
],
"metadata": {
"id": "ijEEXUXztzXD"
},
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import torch"
],
"metadata": {
"id": "3sUL_mpAvywU"
},
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"source": [
"DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"\n",
"# Load different ckpts via passing e.g. `revision=step10000-tokens41B`\n",
"model = OlmoeForCausalLM.from_pretrained(\"allenai/OLMoE-1B-7B-0924\").to(DEVICE)\n",
"tokenizer = AutoTokenizer.from_pretrained(\"allenai/OLMoE-1B-7B-0924\")\n",
"inputs = tokenizer(\"Bitcoin is\", return_tensors=\"pt\")\n",
"inputs = {k: v.to(DEVICE) for k, v in inputs.items()}\n",
"out = model.generate(**inputs, max_length=64)\n",
"print(tokenizer.decode(out[0]))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"9074c90ec0f74bf29bf36d65ebba1b96",
"39038e915d234800bcaaa9b3895f1b68",
"6d3b0f9e6c494cbea42456b60b011564",
"831274f9a0ec40668ec7fcc5ac2771c3",
"8fa5eaaf1c8d4bc18ee076d33d563a0a",
"4a614b527a2a49789d41df08c3466764",
"30e7f062bc1b44a3a99363cb1a06fea9",
"528eadb5cd604eb78b99b3781ad8a357",
"4f5a0839fd414dbaa5a2f6306cfa5ecc",
"d44816c885194cef98b86893263b9b10",
"9c8da3b512bf414684a22b77d7fe48c6"
]
},
"id": "Kj8Ti_I_wcvc",
"outputId": "4419620d-f678-4f1c-9e27-593298f9c0ed"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9074c90ec0f74bf29bf36d65ebba1b96"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "R0VMDS_Hwm83"
},
"execution_count": null,
"outputs": []
}
]
} |