From 56bc7ce3a2d7db268b103ad127589b3b7a714197 Mon Sep 17 00:00:00 2001
From: kyo <113977534+kyo-takano@users.noreply.github.com>
Date: Thu, 11 Apr 2024 10:18:55 +0900
Subject: [PATCH] Fix a typo in  `Gemma_Distributed_Fine_tuning_on_TPU.ipynb`

---
 notebooks/Gemma_Distributed_Fine_tuning_on_TPU.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/notebooks/Gemma_Distributed_Fine_tuning_on_TPU.ipynb b/notebooks/Gemma_Distributed_Fine_tuning_on_TPU.ipynb
index 892e62cb..2db9079a 100644
--- a/notebooks/Gemma_Distributed_Fine_tuning_on_TPU.ipynb
+++ b/notebooks/Gemma_Distributed_Fine_tuning_on_TPU.ipynb
@@ -48,7 +48,7 @@
       "source": [
         "## Overview\n",
         "\n",
-        "Gemma is a family of lightweight, state-of-the-art open models built from research and technology used to create Google Gemini models. Gemma can be further finetuned to suit specific needs. But Large Language Models, such as Gemma, can be very large in size and some of them may not fit on a sing accelerator for finetuning. In this case there are two general approaches for finetuning them:\n",
+        "Gemma is a family of lightweight, state-of-the-art open models built from research and technology used to create Google Gemini models. Gemma can be further finetuned to suit specific needs. But Large Language Models, such as Gemma, can be very large in size and some of them may not fit on a single accelerator for finetuning. In this case there are two general approaches for finetuning them:\n",
         "1. Parameter Efficient Fine-Tuning (PEFT), which seeks to shrink the effective model size by sacrificing some fidelity. LoRA falls in this category and the [Fine-tune Gemma models in Keras using LoRA](https://ai.google.dev/gemma/docs/lora_tuning) tutorial demonstrates how to finetune the Gemma 7B model `gemma_instruct_7b_en` with LoRA using KerasNLP on a single GPU.\n",
         "2. Full parameter finetuning with model parallelism. Model parallelism distributes a single model's weights across multiple devices and enables horizontal scaling. You can find out more about distributed training in this [Keras guide](https://keras.io/guides/distribution/).\n",
         "\n",
@@ -4232,4 +4232,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}