{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# |export\n", "import gradio as gr\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# |export\n", "df = pd.read_csv(\"https://docs.google.com/spreadsheets/d/e/2PACX-1vSC40sszorOjHfozmNqJT9lFiJhG94u3fbr3Ss_7fzcU3xqqJQuW1Ie_SNcWEB-uIsBi9NBUK7-ddet/pub?output=csv\", skiprows=1)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# |export\n", "# Drop footers\n", "df = df.copy()[~df[\"Model\"].isna()]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "# |export\n", "# Drop TBA models\n", "df = df.copy()[df[\"Parameters \\n(B)\"] != \"TBA\"]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ModelLabSelected \\nplaygroundsParameters \\n(B)Tokens \\ntrained (B)Ratio T:P\\n(Chinchilla scaling)Training datasetAnnounced\\nβ–ΌPublic?ReleasedPaper/\\nRepoNotes
2KOSMOS-1MicrosoftNaN1.6360225:1πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹Feb/2023πŸ”΄Feb/2023πŸ”—Multimodal large language model (MLLM). Raven’...
3LLaMA-65BMeta AIhttps://research.facebook.com/publications/lla...65140022:1πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹Feb/2023🟑Feb/2023πŸ”—Researchers only, noncommercial only. 'LLaMA-6...
4MOSSFudan Universityhttps://moss.fastnlp.top/2043022:1πŸ•Έ πŸŒ‹Feb/2023🟒Feb/2023πŸ”—Major bandwidth issues: https://www.reuters.co...
5Luminous Supreme ControlAleph Alphahttps://app.aleph-alpha.com/playground/completion70NaNNaNπŸ†† πŸ“šβ¬† πŸ•Έ πŸ‘₯Feb/2023🟒Feb/2023πŸ”—β€˜Control’ means instruction tuned
6Multimodal-CoTAmazonhttps://github.com/amazon-science/mm-cot0.738NaNNaNπŸŒ‹Feb/2023🟒Feb/2023πŸ”—Models <1B with vision CoT
\n", "
" ], "text/plain": [ " Model Lab \\\n", "2 KOSMOS-1 Microsoft \n", "3 LLaMA-65B Meta AI \n", "4 MOSS Fudan University \n", "5 Luminous Supreme Control Aleph Alpha \n", "6 Multimodal-CoT Amazon \n", "\n", " Selected \\nplaygrounds Parameters \\n(B) \\\n", "2 NaN 1.6 \n", "3 https://research.facebook.com/publications/lla... 65 \n", "4 https://moss.fastnlp.top/ 20 \n", "5 https://app.aleph-alpha.com/playground/completion 70 \n", "6 https://github.com/amazon-science/mm-cot 0.738 \n", "\n", " Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset \\\n", "2 360 225:1 πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹ \n", "3 1400 22:1 πŸ†† πŸ“šβ¬† πŸ•Έ πŸŒ‹ \n", "4 430 22:1 πŸ•Έ πŸŒ‹ \n", "5 NaN NaN πŸ†† πŸ“šβ¬† πŸ•Έ πŸ‘₯ \n", "6 NaN NaN πŸŒ‹ \n", "\n", " Announced\\nβ–Ό Public? Released Paper/\\nRepo \\\n", "2 Feb/2023 πŸ”΄ Feb/2023 πŸ”— \n", "3 Feb/2023 🟑 Feb/2023 πŸ”— \n", "4 Feb/2023 🟒 Feb/2023 πŸ”— \n", "5 Feb/2023 🟒 Feb/2023 πŸ”— \n", "6 Feb/2023 🟒 Feb/2023 πŸ”— \n", "\n", " Notes \n", "2 Multimodal large language model (MLLM). Raven’... \n", "3 Researchers only, noncommercial only. 'LLaMA-6... \n", "4 Major bandwidth issues: https://www.reuters.co... \n", "5 β€˜Control’ means instruction tuned \n", "6 Models <1B with vision CoT " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ModelLabSelected \\nplaygroundsParameters \\n(B)Tokens \\ntrained (B)Ratio T:P\\n(Chinchilla scaling)Training datasetAnnounced\\nβ–ΌPublic?ReleasedPaper/\\nRepoNotes
88MeenaGoogleNaN2.6100003,847:1πŸ‘₯ πŸŒ‹Jan/2020πŸ”΄Jan/2020πŸ”—Dialogue model. Trained 61B tokens for 164x ep...
89RoBERTaMeta AIHugging Face0.35522006,198:1πŸ†† πŸ“š ⬆ πŸ•ΈJul/2019🟒Jul/2019πŸ”—See cite ROBERTA
90GPT-2OpenAIHugging Face1.5107:1⬆Feb/2019🟒Nov/2019πŸ”—Reddit outbound only
91GPT-1OpenAIHugging Face0.1NaNNaNπŸ“šJun/2018🟒Jun/2018πŸ”—Books only
92BERTGoogleHugging Face0.3137457:1πŸ†† πŸ“šOct/2018🟒Oct/2018πŸ”—NaN
\n", "
" ], "text/plain": [ " Model Lab Selected \\nplaygrounds Parameters \\n(B) \\\n", "88 Meena Google NaN 2.6 \n", "89 RoBERTa Meta AI Hugging Face 0.355 \n", "90 GPT-2 OpenAI Hugging Face 1.5 \n", "91 GPT-1 OpenAI Hugging Face 0.1 \n", "92 BERT Google Hugging Face 0.3 \n", "\n", " Tokens \\ntrained (B) Ratio T:P\\n(Chinchilla scaling) Training dataset \\\n", "88 10000 3,847:1 πŸ‘₯ πŸŒ‹ \n", "89 2200 6,198:1 πŸ†† πŸ“š ⬆ πŸ•Έ \n", "90 10 7:1 ⬆ \n", "91 NaN NaN πŸ“š \n", "92 137 457:1 πŸ†† πŸ“š \n", "\n", " Announced\\nβ–Ό Public? Released Paper/\\nRepo \\\n", "88 Jan/2020 πŸ”΄ Jan/2020 πŸ”— \n", "89 Jul/2019 🟒 Jul/2019 πŸ”— \n", "90 Feb/2019 🟒 Nov/2019 πŸ”— \n", "91 Jun/2018 🟒 Jun/2018 πŸ”— \n", "92 Oct/2018 🟒 Oct/2018 πŸ”— \n", "\n", " Notes \n", "88 Dialogue model. Trained 61B tokens for 164x ep... \n", "89 See cite ROBERTA \n", "90 Reddit outbound only \n", "91 Books only \n", "92 NaN " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# |export\n", "title = \"\"\"

The Large Language Models Landscape

\"\"\"\n", "description = \"\"\"Large Language Models (LLMs) today come in a variety architectures and capabilities. This interactive landscape provides a visual overview of the most important LLMs, including their training data, size, release date, and whether they are openly accessible or not. It also includes notes on each model to provide additional context. This landscape is derived from data compiled by Dr. Alan D. Thompson at [lifearchitect.ai](https://lifearchitect.ai).\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running on local URL: http://127.0.0.1:7862\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# |export\n", "def value_func():\n", " return df\n", "\n", "with gr.Blocks() as demo:\n", " gr.Markdown(title)\n", " gr.Markdown(description)\n", " gr.DataFrame(value=value_func)\n", "\n", "demo.launch()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Closing server running on port: 7862\n" ] } ], "source": [ "demo.close()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "from nbdev.export import nb_export\n", "\n", "nb_export(\"app.ipynb\", lib_path=\".\", name=\"app\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "hf", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.13" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "66e5af1d4a3a75efffc7cd5a7f382675fc3ac06b0697676e06fa85c907378a99" } } }, "nbformat": 4, "nbformat_minor": 2 }