diff --git a/docs/Setup.md b/docs/Setup.md
index 35235f2..f123c7f 100644
--- a/docs/Setup.md
+++ b/docs/Setup.md
@@ -70,7 +70,7 @@ Pressing `Submit` will download the model from HuggingFace. The downloaded files
 ### Step 3: Model Configuration
 This step allows you to configure how the model is "prompted". See [here](./Model%20Prompting.md) for more information on how that works.
 
-For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you must select the `Home-LLM (v1-v3)` API. This API is included to ensure compatability with the Home-LLM models that were trained before the introduction of the built in Home Assistant LLM API.
+For now, defaults for the model should have been populated. If you would like the Home-LLM model to be able to control devices then you should select the `Home-LLM (v1-v3)` API. This API is included to ensure compatability with the Home-LLM models that were trained before the introduction of the built in Home Assistant LLM API.
 
 Once the desired API has been selected, scroll to the bottom and click `Submit`.
 
@@ -93,8 +93,13 @@ There are multiple size options for the Qwen3 series of model. Replace `8b` with
 
 Qwen3 can be easily set up and downloaded on the serving machine using the `ollama pull qwen3:8b` command.
 
-In order to access the model from another machine, we need to run the Ollama API server open to the local network. This can be achieved using the `OLLAMA_HOST=0.0.0.0:11434 ollama serve` command. **DO NOT RUN THIS COMMAND ON ANY PUBLICLY
- ACCESSIBLE SERVERS AS IT LISTENS ON ALL NETWORK INTERFACES**
+> Note: You can also host the Home-LLM models on Ollama by pulling them from HuggingFace directly by prepending `hf.co/` to the full model name. For example:
+>  - `acon96/Home-3B-v3-GGUF` -> `ollama pull hf.co/acon96/Home-3B-v3-GGUF`
+>  - `acon96/Home-1B-v3-GGUF` -> `ollama pull hf.co/acon96/Home-1B-v3-GGUF`
+
+In order to access the model from another machine, we need to run the Ollama API server open to the local network. This can be achieved using the `OLLAMA_HOST=0.0.0.0:11434 ollama serve` command. 
+
+**DO NOT RUN THIS COMMAND ON ANY PUBLICLY ACCESSIBLE SERVERS AS IT LISTENS ON ALL NETWORK INTERFACES**
 
 ### Step 2: Connect to the Ollama API
 
@@ -112,11 +117,11 @@ In order to access the model from another machine, we need to run the Ollama API
 
 ### Step 3: Model Selection & Configuration
 1. You must create the conversation agent based on the model you wish to use.  
-    Under the `Ollama at '<url>` service that you just created, select `+ Add conversation agent`  
+    Under the `Ollama at '<url>'` service that you just created, select `+ Add conversation agent`  
     - **Model Name**: Select `qwen3:8b` from the list.
 2. You can configure how the model is "prompted". See [here](./Model%20Prompting.md) for more information on how that works.  
 
-For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you must select the `Assist` API.
+For now, defaults for the model should have been populated. If you would like the Qwen3 model to be able to control devices then you should select the `Assist` API. This is the included Home Assistant API for controlling devices via Large Language Models.
 
 Once the desired model has been selected & configured, scroll to the bottom and click `Submit`.
 
@@ -154,7 +159,7 @@ Llama 3 8B can be set up and downloaded on the serving machine using LM Studio b
     - - **Model Name**: Set this to the name of the model as it appears in LM Studio. The dropdown list should pre-populate with the models that are already installed.
 2. You can configure how the model is "prompted". See [here](./Model%20Prompting.md) for more information on how that works.  
 
-For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you must select the `Assist` API.
+For now, defaults for the model should have been populated. If you would like the model to be able to control devices then you should select the `Assist` API. This is the included Home Assistant API for controlling devices via Large Language Models.
 
 > NOTE: The key settings in this case are that our prompt references the `{{ response_examples }}` variable and the `Enable in context learning (ICL) examples` option is turned on.