From b641e0e40fe8117802086e8966158afa2cc32f36 Mon Sep 17 00:00:00 2001
From: Costa Shulyupin <costa.shul@redhat.com>
Date: Sat, 1 Feb 2025 21:17:51 +0200
Subject: [PATCH] [docs] fix links to PEFT guides

---
 docs/source/index.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/source/index.md b/docs/source/index.md
index cfb57c0678..6e083647b5 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -26,11 +26,11 @@ PEFT is integrated with the Transformers, Diffusers, and Accelerate libraries to
       ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Get started</div>
       <p class="text-gray-700">Start here if you're new to 🤗 PEFT to get an overview of the library's main features, and how to train a model with a PEFT method.</p>
     </a>
-    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./task_guides/image_classification_lora"
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./task_guides/prompt_based_methods"
       ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
       <p class="text-gray-700">Practical guides demonstrating how to apply various PEFT methods across different types of tasks like image classification, causal language modeling, automatic speech recognition, and more. Learn how to use 🤗 PEFT with the DeepSpeed and Fully Sharded Data Parallel scripts.</p>
     </a>
-    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual_guides/lora"
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual_guides/adapter"
       ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
       <p class="text-gray-700">Get a better theoretical understanding of how LoRA and various soft prompting methods help reduce the number of trainable parameters to make training more efficient.</p>
    </a>