Move up ### Voice Mode in README.MD

Author: joshbickett

Diff for: README.md

@@ -113,26 +113,6 @@ operate -m llava
 
 Learn more about Ollama at its [GitHub Repository](https://www.github.com/ollama/ollama)
 
-### Optical Character Recognition Mode `-m gpt-4-with-ocr`
-The Self-Operating Computer Framework now integrates Optical Character Recognition (OCR) capabilities with the `gpt-4-with-ocr` mode. This mode gives GPT-4 a hash map of clickable elements by coordinates. GPT-4 can decide to `click` elements by text and then the code references the hash map to get the coordinates for that element GPT-4 wanted to click. 
-
-Based on recent tests, OCR performs better than `som` and vanilla GPT-4 so we made it the default for the project. To use the OCR mode you can simply write: 
-
- `operate` or `operate -m gpt-4-with-ocr` will also work. 
-
-### Set-of-Mark Prompting `-m gpt-4-with-som`
-The Self-Operating Computer Framework now supports Set-of-Mark (SoM) Prompting with the `gpt-4-with-som` command. This new visual prompting method enhances the visual grounding capabilities of large multimodal models.
-
-Learn more about SoM Prompting in the detailed arXiv paper: [here](https://arxiv.org/abs/2310.11441).
-
-For this initial version, a simple YOLOv8 model is trained for button detection, and the `best.pt` file is included under `model/weights/`. Users are encouraged to swap in their `best.pt` file to evaluate performance improvements. If your model outperforms the existing one, please contribute by creating a pull request (PR).
-
-Start `operate` with the SoM model
-
-```
-operate -m gpt-4-with-som
-```
-
 ### Voice Mode `--voice`
 The framework supports voice inputs for the objective. Try voice by following the instructions below. 
 **Clone the repo** to a directory on your computer:
@@ -161,6 +141,28 @@ Run with voice mode
 operate --voice
 ```
 
+### Optical Character Recognition Mode `-m gpt-4-with-ocr`
+The Self-Operating Computer Framework now integrates Optical Character Recognition (OCR) capabilities with the `gpt-4-with-ocr` mode. This mode gives GPT-4 a hash map of clickable elements by coordinates. GPT-4 can decide to `click` elements by text and then the code references the hash map to get the coordinates for that element GPT-4 wanted to click. 
+
+Based on recent tests, OCR performs better than `som` and vanilla GPT-4 so we made it the default for the project. To use the OCR mode you can simply write: 
+
+ `operate` or `operate -m gpt-4-with-ocr` will also work. 
+
+### Set-of-Mark Prompting `-m gpt-4-with-som`
+The Self-Operating Computer Framework now supports Set-of-Mark (SoM) Prompting with the `gpt-4-with-som` command. This new visual prompting method enhances the visual grounding capabilities of large multimodal models.
+
+Learn more about SoM Prompting in the detailed arXiv paper: [here](https://arxiv.org/abs/2310.11441).
+
+For this initial version, a simple YOLOv8 model is trained for button detection, and the `best.pt` file is included under `model/weights/`. Users are encouraged to swap in their `best.pt` file to evaluate performance improvements. If your model outperforms the existing one, please contribute by creating a pull request (PR).
+
+Start `operate` with the SoM model
+
+```
+operate -m gpt-4-with-som
+```
+
+
+
 ## Contributions are Welcomed!:
 
 If you want to contribute yourself, see [CONTRIBUTING.md](https://github.com/OthersideAI/self-operating-computer/blob/main/CONTRIBUTING.md).