diff --git a/docs/source/installation.mdx b/docs/source/installation.mdx
index 2f8fe4db7..f917f2623 100644
--- a/docs/source/installation.mdx
+++ b/docs/source/installation.mdx
@@ -2,7 +2,7 @@
 
 ## CUDA
 
-bitsandbytes is only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. There's a multi-backend effort under way which is currently in alpha release, see further down in this document.
+bitsandbytes is only supported on CUDA GPUs for CUDA versions **11.0 - 12.5**. However, there's a multi-backend effort under way which is currently in alpha release, check [the respective section below in case you're interested to help us with early feedback](#multi-backend).
 
 The latest version of bitsandbytes builds on:
 
@@ -134,7 +134,7 @@ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/YOUR_USERNAME/local/cuda-11.7
 
 3. Now when you launch bitsandbytes with these environment variables, the PyTorch CUDA version is overridden by the new CUDA version (in this example, version 11.7) and a different bitsandbytes library is loaded.
 
-## Multi-backend preview release (+ compilation)
+## Multi-backend preview release compilation[[multi-backend]]
 
 Please follow these steps to install bitsandbytes with device-specific backend support other than CUDA:
 
@@ -143,11 +143,10 @@ Please follow these steps to install bitsandbytes with device-specific backend s
 
 ### AMD GPU
 
-For a ROCm specific install:
+bitsandbytes is fully supported from ROCm 6.1 onwards (currently in alpha release).
 
-bitsandbytes is fully supported from ROCm 6.1.
-
-**Note:** If you already installed ROCm and PyTorch, skip docker steps below and please check that the torch version matches your ROCm install. To install torch for a specific ROCm version, please refer to step 3 of wheels install in [Installing PyTorch for ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/3rd-party/pytorch-install.html#using-wheels-package) guide.
+> [!TIP]
+> If you already installed ROCm and PyTorch, skip Docker steps below and please check that the torch version matches your ROCm install. To install torch for a specific ROCm version, please refer to step 3 of wheels install in [Installing PyTorch for ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/how-to/3rd-party/pytorch-install.html#using-wheels-package) guide.
 
 ```bash
 # Create a docker container with latest pytorch. It comes with ROCm and pytorch preinstalled
@@ -161,6 +160,7 @@ git clone --depth 1 -b multi-backend-refactor https://github.com/TimDettmers/bit
 pip install -r requirements-dev.txt
 
 # Compile & install
+apt-get install -y build-essential cmake  # install build tools dependencies, unless present
 cmake -DCOMPUTE_BACKEND=hip -S .  # Use -DBNB_ROCM_ARCH="gfx90a;gfx942" to target specific gpu arch
 make
 pip install -e .   # `-e` for "editable" install, when developing BNB (otherwise leave that out)
@@ -179,7 +179,7 @@ Similar to the CUDA case, you can compile bitsandbytes from source for Linux and
 The below commands are for Linux. For installing on Windows, please adapt the below commands according to the same pattern as described [the section above on compiling from source under the Windows tab](#compile).
 
 ```
-git clone --branch multi-backend-refactor https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
+git clone --depth 1 -b multi-backend-refactor https://github.com/TimDettmers/bitsandbytes.git && cd bitsandbytes/
 pip install intel_extension_for_pytorch
 pip install -r requirements-dev.txt
 cmake -DCOMPUTE_BACKEND=cpu -S .