update README.md and LLaMA_LoRA.ipynb

LLaMA_LoRA.ipynb

@@ -6,7 +6,7 @@
       "provenance": [],
       "private_outputs": true,
       "toc_visible": true,
-      "authorship_tag": "ABX9TyOQclbOikH+aaXWlbaTkEqc",
+      "authorship_tag": "ABX9TyMHMc4PwWLbRlhFol+WRzoT",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -34,7 +34,8 @@
       "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."
+        "\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"
@@ -74,7 +75,7 @@
         "# @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",
+        "llama_lora_project_branch = \"main\" # @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"
@@ -309,41 +310,6 @@
       },
       "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

@@ -15,6 +15,100 @@ Making evaluating and fine-tuning LLaMA models with low-rank adaptation (LoRA) e
   * 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.
 
 
+## How to Start
+
+There are various ways to run this app:
+
+* **[Run on Google Colab](#run-on-google-colab)**: The simplest way to get started, all you need is a Google account. Standard (free) GPU runtime is sufficient to run generation and training with micro batch size of 8. However, the text generation and training is much slower than on other cloud services, and Colab might terminate the execution in inactivity while running long tasks.
+* **[Run on a cloud service via SkyPilot](#run-on-a-cloud-service-via-skypilot)**: If you have a cloud service (Lambda Labs, GCP, AWS, or Azure) account, you can use SkyPilot to run the app on a cloud service. A cloud bucket can be mounted to preserve your data.
+* **[Run locally](#run-locally)**: Depends on the hardware you have.
+
+### Run On Google Colab
+
+Open [this Colab Notebook](https://colab.research.google.com/github/zetavg/LLaMA-LoRA/blob/main/LLaMA_LoRA.ipynb) and select **Runtime > Run All** (`⌘/Ctrl+F9`).
+
+You will be prompted to authorize Google Drive access, as Google Drive will be used to store your data. See the "Config"/"Google Drive" section for settings and more info.
+
+After approximately 5 minutes of running, you will see the public URL in the output of the "Launch"/"Start Gradio UI 🚀" section (like `Running on public URL: https://xxxx.gradio.live`). Open the URL in your browser to use the app.
+
+### Run on a cloud service via SkyPilot
+
+After following the [installation guide of SkyPilot](https://skypilot.readthedocs.io/en/latest/getting-started/installation.html), create a `.yaml` to define a task for running the app:
+
+```yaml
+# llama-lora-multitool.yaml
+
+resources:
+  accelerators: A10:1  # 1x NVIDIA A10 GPU
+  cloud: lambda  # Optional; if left out, SkyPilot will automatically pick the cheapest cloud.
+
+file_mounts:
+  # Mount a presisted cloud storage that will be used as the data directory.
+  # (to store train datasets trained models)
+  # See https://skypilot.readthedocs.io/en/latest/reference/storage.html for details.
+  /data:
+    name: llama-lora-multitool-data  # Make sure this name is unique or you own this bucket. If it does not exists, SkyPilot will try to create a bucket with this name.
+    store: gcs  # Could be either of [s3, gcs]
+    mode: MOUNT
+
+# Clone the LLaMA-LoRA repo and install its dependencies.
+setup: |
+  git clone https://github.com/zetavg/LLaMA-LoRA.git llama_lora
+  cd llama_lora && pip install -r requirements.txt
+  cd ..
+  echo 'Dependencies installed.'
+
+# Start the app.
+run: |
+  echo 'Starting...'
+  python llama_lora/app.py --data_dir='/data' --base_model='decapoda-research/llama-7b-hf' --share
+```
+
+Then launch a cluster to run the task:
+
+```
+sky launch -c llama-lora-multitool llama-lora-multitool.yaml
+```
+
+`-c ...` is an optional flag to specify a cluster name. If not specified, SkyPilot will automatically generate one.
+
+You will see the public URL of the app in the terminal. Open the URL in your browser to use the app.
+
+Note that exiting `sky launch` will only exit log streaming and will not stop the task. You can use `sky queue --skip-finished` to see the status of running or pending tasks, `sky logs <cluster_name> <job_id>` connect back to log streaming, and `sky cancel <cluster_name> <job_id>` to stop a task.
+
+When you are done, run `sky stop <cluster_name>` to stop the cluster. To terminate a cluster instead, run `sky down <cluster_name>`.
+
+### Run locally
+
+<details>
+  <summary>Prepare environment with conda</summary>
+
+  ```bash
+  conda create -y -n llama-lora-multitool python=3.8
+  conda activate llama-lora-multitool
+  ```
+</details>
+
+```bash
+pip install -r requirements.txt
+python app.py --data_dir='./data' --base_model='decapoda-research/llama-7b-hf' --share
+```
+
+You will see the local and public URLs of the app in the terminal. Open the URL in your browser to use the app.
+
+For more options, see `python app.py --help`.
+
+<details>
+  <summary>UI development mode</summary>
+
+  To test the UI without loading the language model, use the `--ui_dev_mode` flag:
+
+  ```bash
+  python app.py --data_dir='./data' --base_model='decapoda-research/llama-7b-hf' --share --ui_dev_mode
+  ```
+</details>
+
+
 ## Acknowledgements
 
 * https://github.com/tloen/alpaca-lora