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@ariG23498
Last active November 18, 2024 00:31
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Demo LoLCATs LLMs
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ariG23498/45b0c2afc95ca4c4b7cf64fbc161c1e7/scratchpad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"## Clone the Repository"
],
"metadata": {
"id": "gcBzM4xCMOIW"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lIYdn1woOS1n",
"outputId": "3fd7f3b4-6802-4110-9e50-6ccccbb4c3bd"
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'lolcats'...\n",
"remote: Enumerating objects: 3794, done.\u001b[K\n",
"remote: Counting objects: 100% (356/356), done.\u001b[K\n",
"remote: Compressing objects: 100% (163/163), done.\u001b[K\n",
"remote: Total 3794 (delta 219), reused 314 (delta 192), pack-reused 3438 (from 1)\u001b[K\n",
"Receiving objects: 100% (3794/3794), 95.30 MiB | 17.21 MiB/s, done.\n",
"Resolving deltas: 100% (2201/2201), done.\n",
"/content/lolcats\n"
]
}
],
"source": [
"! git clone https://github.com/HazyResearch/lolcats.git\n",
"%cd lolcats"
]
},
{
"cell_type": "markdown",
"source": [
"## Setup and Install"
],
"metadata": {
"id": "kyYR7X02MQW-"
}
},
{
"cell_type": "code",
"source": [
"!pip install -Uq omegaconf peft"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wywFiknZHSjs",
"outputId": "d9015298-f959-491f-c263-c3cd06ff7b5e"
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"execution_count": 2,
"outputs": [
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"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/117.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.0/117.0 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m79.5/79.5 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.7/320.7 kB\u001b[0m \u001b[31m22.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## Login to Hugging Face"
],
"metadata": {
"id": "56lmL_zyMT-k"
}
},
{
"cell_type": "code",
"source": [
"from huggingface_hub import login"
],
"metadata": {
"id": "pCvi38PwHvoH"
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"execution_count": 3,
"outputs": []
},
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"login()"
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"output_type": "display_data",
"data": {
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"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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{
"cell_type": "markdown",
"source": [
"## Run the demo\n",
"\n",
"Before running the demo, open the `demos/demo_8b.sh` and change the `CONFIG_DIR`. The `CONFIG_DIR` should point to the configuration directory."
],
"metadata": {
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"!cd demos && bash demo_8b.sh"
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"outputs": []
}
],
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"value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
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@ariG23498
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You will find more information about the models and how to run other demo in their official repository

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