From 35eac0df75c692c5b93c12f7eaf3279cab8bd7ce Mon Sep 17 00:00:00 2001
From: Marc Sun <57196510+SunMarc@users.noreply.github.com>
Date: Mon, 10 Jul 2023 17:49:30 -0400
Subject: [PATCH] add link to accelerate doc (#24601)

---
 docs/source/en/main_classes/quantization.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/source/en/main_classes/quantization.md b/docs/source/en/main_classes/quantization.md
index eb360e603a..8984ac4787 100644
--- a/docs/source/en/main_classes/quantization.md
+++ b/docs/source/en/main_classes/quantization.md
@@ -25,6 +25,8 @@ Learn more about the quantization method in the [LLM.int8()](https://arxiv.org/a
 
 Since its `0.39.0` release, you can load any model that supports `device_map` using 4-bit quantization, leveraging FP4 data type.
 
+If you want to quantize your own pytorch model, check out this [documentation](https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization) from 🤗 Accelerate library. 
+
 Here are the things you can do using `bitsandbytes` integration
 
 ### FP4 quantization 
@@ -269,4 +271,3 @@ Note that you don't need to pass `device_map` when loading the model for trainin
 ## Quantization with 🤗 `optimum` 
 
 Please have a look at [Optimum documentation](https://huggingface.co/docs/optimum/index) to learn more about quantization methods that are supported by `optimum` and see if these are applicable for your usecase.
-