rename project

LLaMA_LoRA.ipynb

@@ -27,13 +27,13 @@
         "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>"
+        "<a href=\"https://colab.research.google.com/github/zetavg/LLaMA-LoRA-Tuner/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",
+        "# 🦙🎛️ LLaMA-LoRA Tuner\n",
         "\n",
         "TL;DR: **Runtime > Run All** (`⌘/Ctrl+F9`). Takes about 5 minutes to start. You will be promped to authorize Google Drive access."
       ],
@@ -72,9 +72,9 @@
         "# @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",
+        "# @markdown The URL of the LLaMA-LoRA-Tuner project<br>&nbsp;&nbsp;(default: `https://github.com/zetavg/LLaMA-LoRA-Tuner.git`):\n",
+        "llama_lora_project_url = \"https://github.com/zetavg/LLaMA-LoRA-Tuner.git\" # @param {type:\"string\"}\n",
+        "# @markdown The branch to use for LLaMA-LoRA-Tuner project:\n",
         "llama_lora_project_branch = \"main\" # @param {type:\"string\"}\n",
         "\n",
         "# # @markdown Forces the local directory to be updated by the remote branch:\n",

README.md

@@ -1,6 +1,6 @@
-# 🦙🎛️ LLaMA-LoRA
+# 🦙🎛️ LLaMA-LoRA Tuner
 
-<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>
+<a href="https://colab.research.google.com/github/zetavg/LLaMA-LoRA-Tuner/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.
 
@@ -27,7 +27,7 @@ There are various ways to run this app:
 
 ### 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`).
+Open [this Colab Notebook](https://colab.research.google.com/github/zetavg/LLaMA-LoRA-Tuner/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.
 
@@ -38,7 +38,7 @@ After approximately 5 minutes of running, you will see the public URL in the out
 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
+# llama-lora-tuner.yaml
 
 resources:
   accelerators: A10:1  # 1x NVIDIA A10 GPU
@@ -49,13 +49,13 @@ file_mounts:
   # (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.
+    name: llama-lora-tuner-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.
+# Clone the LLaMA-LoRA Tuner repo and install its dependencies.
 setup: |
-  git clone https://github.com/zetavg/LLaMA-LoRA.git llama_lora
+  git clone https://github.com/zetavg/LLaMA-LoRA-Tuner.git llama_lora
   cd llama_lora && pip install -r requirements.lock.txt
   cd ..
   echo 'Dependencies installed.'
@@ -69,7 +69,7 @@ run: |
 Then launch a cluster to run the task:
 
 ```
-sky launch -c llama-lora-multitool llama-lora-multitool.yaml
+sky launch -c llama-lora-tuner llama-lora-tuner.yaml
 ```
 
 `-c ...` is an optional flag to specify a cluster name. If not specified, SkyPilot will automatically generate one.
@@ -86,8 +86,8 @@ When you are done, run `sky stop <cluster_name>` to stop the cluster. To termina
   <summary>Prepare environment with conda</summary>
 
   ```bash
-  conda create -y python=3.8 -n llama-lora-multitool
-  conda activate llama-lora-multitool
+  conda create -y python=3.8 -n llama-lora-tuner
+  conda activate llama-lora-tuner
   ```
 </details>
 

llama_lora/globals.py

@@ -41,7 +41,7 @@ class Global:
     gpu_total_memory = None
 
     # UI related
-    ui_title: str = "LLaMA-LoRA"
+    ui_title: str = "LLaMA-LoRA Tuner"
     ui_emoji: str = "🦙🎛️"
     ui_subtitle: str = "Toolkit for evaluating and fine-tuning LLaMA models with low-rank adaptation (LoRA)."
     ui_show_sys_info: bool = True

llama_lora/ui/main_page.py

@@ -30,7 +30,7 @@ def main_page():
                 tokenizer_ui()
             info = []
             if Global.version:
-                info.append(f"LLaMA-LoRA `{Global.version}`")
+                info.append(f"LLaMA-LoRA Tuner `{Global.version}`")
             info.append(f"Base model: `{Global.base_model}`")
             if Global.ui_show_sys_info:
                 info.append(f"Data dir: `{Global.data_dir}`")