diff --git a/tools/README.md b/tools/README.md
index 6d1ee90..ec98261 100644
--- a/tools/README.md
+++ b/tools/README.md
@@ -1,64 +1,66 @@
-## STEP 1 (Patch files with Unix (LF) line endings
+## Converting initial model
 
-Solution to fix lines endings of the patch files from Windows (CRLF) to Unix (LF)
+To convert your initial safetensors/ckpt model to FP16/BF16 GGUF, run the following command:
+
+```
+python convert.py --src E:\models\unet\flux1-dev.safetensors
+```
+Make sure `gguf>=0.13.0` is installed for this step. Optionally, specify the output gguf file with the `--dst` arg.
+
+> [!NOTE]  
+> Do not use the diffusers UNET format for flux, it won't work, use the default/reference checkpoint key format. This is due to q/k/v being merged into one qkv key.
+> You can convert it by loading it in ComfyUI and saving it using the built-in "ModelSave" node.
+
+> [!WARNING] 
+> For hunyuan video/wan 2.1, you will see a warning about 5D tensors. This means the script will save a **non functional** model to disk first, that you can quantize. I recommend saving these in a separate `raw` folder to avoid confusion.
+> 
+> After quantization, you will have to run `fix_5d_tensor.py` manually to add back the missing key that was saved by the conversion code.
+
+## Quantizing using custom llama.cpp
+
+Depending on your git settings, you may need to run the following script first in order to make sure the patch file is valid. It will convert Windows (CRLF) line endings to Unix (LF) ones.
 
 ```
 python fix_lines_ending.py
 ```
-## STEP 2 (Clone llama.cpp version of gguf-py)
 
-Git clone llama.cpp into the current folder. You may also install gguf-py from the llama.cpp repo directly, though the one specified in `requirements.txt` should also work on recent versions.
+Git clone llama.cpp into the current folder:
 
 ```
 git clone https://github.com/ggerganov/llama.cpp
-pip install llama.cpp/gguf-py
 ```
 
-## STEP 3 (Convert to FP16 or BF16)
+Check out the correct branch, then apply the custom patch needed to add image model support to the repo you just cloned.
 
-To convert your initial source model to FP16 (or BF16), run the following command:
-```
-python convert.py --src E:\models\unet\flux1-dev.safetensors
-```
-## STEP 4 (Patch llama.cpp)
-
-- To quantize the model, first apply the provided patch to the llama.cpp repo you've just cloned.
 ```
 cd llama.cpp
 git checkout tags/b3962
 git apply ..\lcpp.patch
 ```
 
+Compile the llama-quantize binary. This example uses cmake, on linux you can just use make.
 
-## STEP 5 (Compile llama-quantize binary)
-
-Then, compile the llama-quantize binary. This example uses cmake, on linux you can just use make.
 ```
 mkdir build
-cd build
-cmake ..
-cmake --build . --config Debug -j10 --target llama-quantize
-cd ..
+cmake -B build
+cmake --build build --config Debug -j10 --target llama-quantize
 cd ..
 ```
 
-## STEP 6 (Quantization)
 Now you can use the newly build binary to quantize your model to the desired format:
 ```
 llama.cpp\build\bin\Debug\llama-quantize.exe E:\models\unet\flux1-dev-BF16.gguf E:\models\unet\flux1-dev-Q4_K_S.gguf Q4_K_S
 ```
 
-
 You can extract the patch again with `git diff src\llama.cpp > lcpp.patch` if you wish to change something and contribute back.
 
 > [!WARNING] 
->For hunyuan video/wan 2.1, you will have to uncomment the block in convert.py that deals with 5D tensors. This will save a **non functional** model to disk first, that you can quantize. After quantization, run `fix_5d_tensor.py` to add back the missing key that was saved by the conversion code. You will have to edit this file to set the correct paths/architecture. This may change in the future.
+> For hunyuan video/wan 2.1, you will have to run `fix_5d_tensor.py` after the quantization step is done.
+>
+> Example usage:  `fix_5d_tensors.py --src E:\models\video\raw\wan2.1-t2v-1.3b-Q8_0.gguf --dst E:\models\video\wan2.1-t2v-1.3b-Q8_0.gguf`
+>
+> By default, this also saves a `fix_5d_tensors_[arch].safetensors` file in the `ComfyUI-GGUF/tools` folder, it's recommended to delete this after all models have been converted.
 
-
-> [!WARNING]  
-> Do not use the diffusers UNET for flux, it won't work, use the default/reference checkpoint format. This is due to q/k/v being merged into one qkv key. You can convert it by loading it in ComfyUI and saving it using the built-in "ModelSave" node.
-
-
-> [!WARNING]  
-> Do not quantize SDXL / SD1 / other Conv2D heavy models. There's little to no benefit with these models. If you do, make sure to **extract the UNET model first**.
+> [!NOTE]
+> Do not quantize SDXL / SD1 / other Conv2D heavy models. If you do, make sure to **extract the UNET model first**.
 >This should be obvious, but also don't use the resulting llama-quantize binary with LLMs.