diff --git a/README.md b/README.md
index f382976..521dea9 100644
--- a/README.md
+++ b/README.md
@@ -7,12 +7,14 @@ Navigate to the root ComfyUI directory, clone the repository to `custom_nodes` a
 git clone https://github.com/Zuellni/ComfyUI-ExLlama-Nodes custom_nodes/ComfyUI-ExLlama-Nodes
 pip install -r custom_nodes/ComfyUI-ExLlama-Nodes/requirements.txt
 ```
-Optionally, you can install [flash-attention](https://github.com/Dao-AILab/flash-attention) by uncommenting the relevant lines in the requirements file. It should lower VRAM usage but your mileage may vary.
-> [!IMPORTANT]
-> The wheels included in the requirements file should match the latest portable ComfyUI build. If you see any ExLlama-related errors while loading the nodes, try to install it manually following the [official instructions](https://github.com/turboderp/exllamav2#installation).
+Optionally, you can install [flash-attention](https://github.com/Dao-AILab/flash-attention) by uncommenting relevant lines in the requirements file. It should lower VRAM usage but your mileage may vary.
+> [!CAUTION]
+> The wheels included in the requirements file should match the `stable pytorch 2.1 cu121` build of ComfyUI.<br>
+> If you see any ExLlama-related errors while loading the nodes, try to install it following the [official instructions](https://github.com/turboderp/exllamav2#installation).<br>
+> Keep in mind that wheels >= `0.0.13` require `pytorch 2.2`.
 
 ## Usage
-Only EXL2 and 4-bit GPTQ models are supported. You can find a lot of them on [Hugging Face](https://huggingface.co/TheBloke). Refer to the model card in each repository for details about quant differences and instruction formats.
+Only EXL2 and 4-bit GPTQ models are supported. You can find a lot of them on [Hugging](https://huggingface.co/LoneStriker) [Face](https://huggingface.co/TheBloke). Refer to the model card in each repository for details about quant differences and instruction formats.
 
 To use a model with the nodes, you should clone its repository with git or manually download all the files and place them in `models/llm`. For example, if you'd like to download [Mistral-7B](https://huggingface.co/LoneStriker/Mistral-7B-Instruct-v0.2-5.0bpw-h6-exl2-2), use the following command:
 ```