From 258dd9cc693d1ad2c43e255cde73ce500d30b303 Mon Sep 17 00:00:00 2001
From: Mohamed Mekkouri <93391238+MekkCyber@users.noreply.github.com>
Date: Wed, 19 Mar 2025 18:56:07 +0100
Subject: [PATCH] Add Space to Bitsandbytes doc (#36834)

* add space

* address review
---
 docs/source/en/quantization/bitsandbytes.md |  5 ++++-
 docs/source/en/quantization/overview.md     | 11 ++++++++++-
 2 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/docs/source/en/quantization/bitsandbytes.md b/docs/source/en/quantization/bitsandbytes.md
index 2f87cb0dcd..e9b581b89f 100644
--- a/docs/source/en/quantization/bitsandbytes.md
+++ b/docs/source/en/quantization/bitsandbytes.md
@@ -20,7 +20,10 @@ rendered properly in your Markdown viewer.
 
 [LLM.int8()](https://hf.co/papers/2208.07339) is a quantization method that aims to make large language model inference more accessible without significant degradation. Unlike naive 8-bit quantization, which can result in loss of critical information and accuracy, LLM.int8() dynamically adapts to ensure sensitive components of the computation retain higher precision when needed.
 
-QLoRA, or 4-bit quantization, compresses a model even further to 4-bits and inserts a small set of trainable low-rank adaptation (LoRA) weights to allowing training.
+QLoRA, or 4-bit quantization, compresses a model even further to 4-bits and inserts a small set of trainable low-rank adaptation (LoRA) weights to allowing training. 
+
+> **Note:** For a user-friendly quantization experience, you can use the `bitsandbytes` [community space](https://huggingface.co/spaces/bnb-community/bnb-my-repo).
+
 
 Run the command below to install bitsandbytes.
 
diff --git a/docs/source/en/quantization/overview.md b/docs/source/en/quantization/overview.md
index 1c0f48988d..d69675e909 100644
--- a/docs/source/en/quantization/overview.md
+++ b/docs/source/en/quantization/overview.md
@@ -46,4 +46,13 @@ Use the Space below to help you pick a quantization method depending on your har
 If you are new to quantization, we recommend checking out these beginner-friendly quantization courses in collaboration with DeepLearning.AI.
 
 * [Quantization Fundamentals with Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/)
-* [Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth
\ No newline at end of file
+* [Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth)
+
+## User-Friendly Quantization Tools
+
+If you are looking for a user-friendly quantization experience, you can use the following community spaces and notebooks: 
+
+* [Bitsandbytes Space](https://huggingface.co/spaces/bnb-community/bnb-my-repo)
+* [GGUF Space](https://huggingface.co/spaces/ggml-org/gguf-my-repo)
+* [MLX Space](https://huggingface.co/spaces/mlx-community/mlx-my-repo)
+* [AuoQuant Notebook](https://colab.research.google.com/drive/1b6nqC7UZVt8bx4MksX7s656GXPM-eWw4?usp=sharing#scrollTo=ZC9Nsr9u5WhN)
\ No newline at end of file