diff --git a/docs/GPTQ-models-(4-bit-mode).md b/docs/GPTQ-models-(4-bit-mode).md
index deb69555..8eaf86ca 100644
--- a/docs/GPTQ-models-(4-bit-mode).md
+++ b/docs/GPTQ-models-(4-bit-mode).md
@@ -1,13 +1,10 @@
-In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
-
-
 GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323
 
 ## AutoGPTQ
 
 AutoGPTQ is the recommended way to create new quantized models: https://github.com/PanQiWei/AutoGPTQ
 
-#### Installation
+### Installation
 
 To load a model quantized with AutoGPTQ in the web UI, manual installation is currently necessary:
 
@@ -19,7 +16,7 @@ pip install .
 
 You are going to need to have `nvcc` installed (see the [instructions below](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-0-install-nvcc)).
 
-#### Usage
+### Usage
 
 Place the output folder generated by AutoGPTQ in your `models/` folder and load it with the `--autogptq` flag:
 
@@ -29,9 +26,9 @@ python server.py --autogptq --model model_name
 
 Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
 
-#### Offloading
+### Offloading
 
-In order to do CPU offloading or multi-cpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA.
+In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA (more on that below).
 
 For CPU offloading:
 
@@ -45,7 +42,7 @@ For multi-GPU:
 python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
 ```
 
-#### Applying LoRAs
+### Using LoRAs with AutoGPTQ
 
 Not supported yet.
 
@@ -63,7 +60,11 @@ Different branches of GPTQ-for-LLaMa are currently available, including:
 
 Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI.
 
-### Installation
+### Installation using precompiled wheels
+
+https://github.com/jllllll/GPTQ-for-LLaMa-Wheels
+
+### Manual installation
 
 #### Step 0: install nvcc
 
@@ -158,18 +159,11 @@ You can also use multiple GPUs with `pre_layer` if using the oobabooga fork of G
 
 ### Using LoRAs with GPTQ-for-LLaMa
 
-At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
+This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
 
-In order to use it:
+To use it:
 
-1. Make sure that your requirements are up to date:
-
-```
-cd text-generation-webui
-pip install -r requirements.txt --upgrade
-```
-
-2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
+1. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
 
 ```
 cd text-generation-webui/repositories
@@ -178,13 +172,13 @@ git clone https://github.com/johnsmith0031/alpaca_lora_4bit
 
 ⚠️  I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0`
 
-3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
+2. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
 
 ```
 pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
 ```
 
-4. Start the UI with the `--monkey-patch` flag:
+3. Start the UI with the `--monkey-patch` flag:
 
 ```
 python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch