update

LLaMA_LoRA.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "private_outputs": true,
+      "toc_visible": true,
+      "authorship_tag": "ABX9TyOQclbOikH+aaXWlbaTkEqc",
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "gpuClass": "standard",
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/zetavg/LLaMA-LoRA/blob/main/LLaMA_LoRA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🦙🎛️ LLaMA-LoRA\n",
+        "\n",        "TL;DR: **Runtime > Run All** (`⌘/Ctrl+F9`). Takes about 5 minutes to start. You will be promped to authorize Google Drive access."
+      ],
+      "metadata": {
+        "id": "bb4nzBvLfZUj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@markdown To prevent Colab from disconnecting you, here is a music player that will loop infinitely (it's silent):\n",
+        "%%html\n",
+        "<audio src=\"https://github.com/anars/blank-audio/raw/master/1-hour-of-silence.mp3\" autoplay muted loop controls />"
+      ],
+      "metadata": {
+        "id": "DwarOgXbG77C",
+        "cellView": "form"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Config\n",
+        "\n",
+        "Some configurations to run this notebook. "
+      ],
+      "metadata": {
+        "id": "5uS5jJ8063f_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Git/Project { display-mode: \"form\", run: \"auto\" }\n",
+        "# @markdown Project settings.\n",
+        "\n",
+        "# @markdown The URL of the LLaMA-LoRA project<br>&nbsp;&nbsp;(default: `https://github.com/zetavg/llama-lora.git`):\n",
+        "llama_lora_project_url = \"https://github.com/zetavg/llama-lora.git\" # @param {type:\"string\"}\n",
+        "# @markdown The branch to use for LLaMA-LoRA project:\n",
+        "llama_lora_project_branch = \"dev-2\" # @param {type:\"string\"}\n",
+        "\n",
+        "# # @markdown Forces the local directory to be updated by the remote branch:\n",
+        "# force_update = True # @param {type:\"boolean\"}\n"
+      ],
+      "metadata": {
+        "id": "v3ZCPW0JBCcH"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Google Drive { display-mode: \"form\", run: \"auto\" }\n",
+        "# @markdown Google Drive will be used to store data that is used or outputed by this notebook. you will be prompted to authorize access while running this notebook. \n",
+        "#\n",
+        "# @markdown Currently, it's not possible to access only a specific folder of Google Drive, we have no choice to mount the entire Google Drive, but will only access given folder.\n",
+        "#\n",
+        "# @markdown You can customize the location of the stored data here.\n",
+        "\n",
+        "# @markdown The folder in Google Drive where Colab Notebook data are stored<br />&nbsp;&nbsp;**(WARNING: The content of this folder will be modified by this notebook)**:\n",
+        "google_drive_folder = \"Colab Data/LLaMA LoRA\" # @param {type:\"string\"}\n",
+        "# google_drive_colab_data_folder = \"Colab Notebooks/Notebook Data\"\n",
+        "\n",
+        "# Where Google Drive will be mounted in the Colab runtime.\n",
+        "google_drive_mount_path = \"/content/drive\"\n",
+        "\n",
+        "from requests import get\n",
+        "from socket import gethostname, gethostbyname\n",
+        "host_ip = gethostbyname(gethostname())\n",
+        "colab_notebook_filename = get(f\"http://{host_ip}:9000/api/sessions\").json()[0][\"name\"]\n",
+        "\n",
+        "# def remove_ipynb_extension(filename: str) -> str:\n",
+        "#     extension = \".ipynb\"\n",
+        "#     if filename.endswith(extension):\n",
+        "#         return filename[:-len(extension)]\n",
+        "#     return filename\n",
+        "\n",
+        "# colab_notebook_name = remove_ipynb_extension(colab_notebook_filename)\n",
+        "\n",
+        "from google.colab import drive\n",
+        "drive.mount(google_drive_mount_path)\n",
+        "\n",
+        "# google_drive_data_directory_relative_path = f\"{google_drive_colab_data_folder}/{colab_notebook_name}\"\n",
+        "google_drive_data_directory_relative_path = google_drive_folder\n",
+        "google_drive_data_directory_path = f\"{google_drive_mount_path}/My Drive/{google_drive_data_directory_relative_path}\"\n",
+        "!mkdir -p \"{google_drive_data_directory_path}\"\n",
+        "!ln -nsf \"{google_drive_data_directory_path}\" ./data\n",
+        "!touch \"data/This folder is used by the Colab notebook \\\"{colab_notebook_filename}\\\".txt\"\n",
+        "!echo \"Data will be stored in Google Drive folder: \\\"{google_drive_data_directory_relative_path}\\\", which is mounted under \\\"{google_drive_data_directory_path}\\\"\"\n"
+      ],
+      "metadata": {
+        "id": "iZmRtUY68U5f"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Model/Training Settings { display-mode: \"form\", run: \"auto\" }\n",
+        "\n",
+        "base_model = \"decapoda-research/llama-7b-hf\" # @param {type:\"string\"}"
+      ],
+      "metadata": {
+        "id": "Ep3Qhwj0Bzwf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Runtime Info\n",
+        "\n",
+        "Print out some information about the Colab runtime. Code from https://colab.research.google.com/notebooks/pro.ipynb."
+      ],
+      "metadata": {
+        "id": "Qg8tzrgb6ya_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AHbRt8sK6YWy"
+      },
+      "outputs": [],
+      "source": [
+        "# @title GPU Info { display-mode: \"form\" }\n",
+        "#\n",
+        "# @markdown By running this cell, you can see what GPU you've been assigned.\n",
+        "#\n",
+        "# @markdown If the execution result of running the code cell below is \"Not connected to a GPU\", you can change the runtime by going to `Runtime / Change runtime type` in the menu to enable a GPU accelerator, and then re-execute the code cell.\n",
+        "\n",
+        "\n",
+        "gpu_info = !nvidia-smi\n",
+        "gpu_info = '\\n'.join(gpu_info)\n",
+        "if gpu_info.find('failed') >= 0:\n",
+        "  print('Not connected to a GPU')\n",
+        "else:\n",
+        "  print(gpu_info)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title RAM Info { display-mode: \"form\" }\n",
+        "#\n",
+        "# @markdown By running this code cell, You can see how much memory you have available.\n",
+        "#\n",
+        "# @markdown Normally, a high-RAM runtime is not needed, but if you need more RAM, you can enable a high-RAM runtime via `Runtime / Change runtime type` in the menu. Then select High-RAM in the Runtime shape dropdown. After, re-execute the code cell.\n",
+        "\n",
+        "from psutil import virtual_memory\n",
+        "ram_gb = virtual_memory().total / 1e9\n",
+        "print('Your runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))\n",
+        "\n",
+        "if ram_gb < 20:\n",
+        "  print('Not using a high-RAM runtime')\n",
+        "else:\n",
+        "  print('You are using a high-RAM runtime!')"
+      ],
+      "metadata": {
+        "id": "rGM5MYjR7yeS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Prepare the Project\n",
+        "\n",
+        "Clone the project and install dependencies (takes about 5m on the first run)."
+      ],
+      "metadata": {
+        "id": "8vSPMNtNAqOo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "![ ! -d llama_lora ] && git clone -b {llama_lora_project_branch} --filter=tree:0 {llama_lora_project_url} llama_lora\n",
+        "!cd llama_lora && git add --all && git stash && git fetch origin {llama_lora_project_branch} && git checkout {llama_lora_project_branch} && git reset origin/{llama_lora_project_branch} --hard\n",
+        "![ ! -f llama-lora-requirements-installed ] && cd llama_lora && pip install -r requirements.txt && touch ../llama-lora-requirements-installed"
+      ],
+      "metadata": {
+        "id": "JGYz2VDoAzC8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Launch"
+      ],
+      "metadata": {
+        "id": "o90p1eYQimyr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# The following command will launch the app in one shot, but we will not do this here.\n",
+        "# Instead, we will import and run Python code from the runtime, so that each part\n",
+        "# can be reloaded easily in the Colab notebook and provide readable outputs.\n",
+        "# It also resolves the GPU out-of-memory issue on training.\n",
+        "# !python llama_lora/app.py --base_model='{base_model}' --data_dir='./data' --share"
+      ],
+      "metadata": {
+        "id": "HYVjcvwXimB6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Load the App (set config, prepare data dir, load base bodel)\n",
+        "\n",
+        "# @markdown For a LLaMA-7B model, it will take about ~5m to load for the first execution,\n",
+        "# @markdown including download. Subsequent executions will take about 2m to load.\n",
+        "\n",
+        "# Set Configs\n",
+        "from llama_lora.llama_lora.globals import Global\n",
+        "Global.base_model = base_model\n",
+        "data_dir_realpath = !realpath ./data\n",
+        "Global.data_dir = data_dir_realpath[0]\n",
+        "Global.load_8bit = True\n",
+        "\n",
+        "# Prepare Data Dir\n",
+        "import os\n",
+        "from llama_lora.llama_lora.utils.data import init_data_dir\n",
+        "init_data_dir()\n",
+        "\n",
+        "# Load the Base Model\n",
+        "from llama_lora.llama_lora.models import load_base_model\n",
+        "load_base_model()\n",
+        "print(f\"Base model loaded: '{Global.base_model}'.\")"
+      ],
+      "metadata": {
+        "id": "Yf6g248ylteP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Start Gradio UI 🚀 (open the app from the URL output-ed here)\n",
+        "\n",
+        "You will see `Running on public URL: https://...` in the output of the following code cell, click on it to open the Gradio UI."
+      ],
+      "metadata": {
+        "id": "K-hCouBClKAe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "from llama_lora.llama_lora.ui.main_page import main_page, get_page_title, main_page_custom_css\n",
+        "\n",
+        "with gr.Blocks(title=get_page_title(), css=main_page_custom_css()) as app:\n",
+        "    main_page()\n",
+        "\n",
+        "app.queue(concurrency_count=1).launch(share=True, debug=True, server_name=\"127.0.0.1\")"
+      ],
+      "metadata": {
+        "id": "iLygNTcHk0N8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Reset"
+      ],
+      "metadata": {
+        "id": "RW09SrCZpqpa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# @title Kill Session { display-mode: \"form\" }\n",
+        "# @markdown If you ran out of runtime resources, you can **check the following \n",
+        "# @markdown checkbox and run this code cell to kill the runtime session** while\n",
+        "# @markdown preserving your downloaded data.\n",
+        "do_kill_session = False # @param {type:\"boolean\"}\n",
+        "# @markdown You will need to re-run this notebook from start after doing this.\n",
+        "#\n",
+        "# @markdown All data that are saved to disk, including Python dependencies, base\n",
+        "# @markdown models will all be preserved, so the second run will be much faster.\n",
+        "\n",
+        "import os\n",
+        "def kill_session():\n",
+        "    os.kill(os.getpid(), 9)\n",
+        "\n",
+        "if do_kill_session:\n",
+        "    kill_session()"
+      ],
+      "metadata": {
+        "id": "bM4sY2tVps8U"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}

README.md

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+# 🦙🎛️ LLaMA-LoRA
+
+<a href="https://colab.research.google.com/github/zetavg/LLaMA-LoRA/blob/main/LLaMA_LoRA.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+
+Making evaluating and fine-tuning LLaMA models with low-rank adaptation (LoRA) easy.
+
+
+## Features
+
+* [1-click up and running in Google Colab](https://colab.research.google.com/github/zetavg/LLaMA-LoRA/blob/main/LLaMA_LoRA.ipynb).
+  * Loads and stores data in Google Drive.
+* Evaluate various LLaMA LoRA models stored in your folder or from Hugging Face.<br /><a href="https://user-images.githubusercontent.com/3784687/230273443-3b1738ca-fbc4-427b-a1e6-9dabca160288.mp4"><img width="640px" src="https://user-images.githubusercontent.com/3784687/230272844-09f7a35b-46bf-4101-b15d-4ddf243b8bef.gif" /></a>
+* Fine-tune LLaMA models with different prompt templates and training dataset format.<br /><a href="https://user-images.githubusercontent.com/3784687/230276439-d729f900-c8c3-47b8-b6f6-93a5cfcecbe8.mp4"><img width="640px" src="https://user-images.githubusercontent.com/3784687/230277315-9a91d983-1690-4594-9d54-912eda8963ee.gif" /></a>
+  * Load JSON and JSONL datasets from your folder, or even paste plain text directly into the UI.
+  * Supports Stanford Alpaca [seed_tasks](https://github.com/tatsu-lab/stanford_alpaca/blob/main/seed_tasks.jsonl), [alpaca_data](https://github.com/tatsu-lab/stanford_alpaca/blob/main/alpaca_data.json) and [OpenAI "prompt"-"completion"](https://platform.openai.com/docs/guides/fine-tuning/data-formatting) format.
+
+
+## Acknowledgements
+
+* https://github.com/tloen/alpaca-lora
+* https://github.com/lxe/simple-llama-finetuner
+* ...
+
+TBC