cleans up README

README.md

@@ -2,9 +2,9 @@
 
 A complete chat app that transcribes audio in real-time, streams back a response from a language model, and synthesizes this response as natural-sounding speech.
 
-This repo is meant to serve as a starting point for your own language model-based apps, as well as a playground for experimentation. Contributions are welcome and encouraged!
+[This repo](https://github.com/modal-labs/quillman) is meant to serve as a starting point for your own language model-based apps, as well as a playground for experimentation. Contributions are welcome and encouraged!
 
-OpenAI [Whisper V3](https://huggingface.co/openai/whisper-large-v3) is used to produce a transcript, which is then passed into the [Llama 3.1 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) language model to generate a response, which is then synthesized by Coqui's [XTTS](https://github.com/coqui-ai/TTS) text-to-speech model. All together, this produces a voice-to-voice chat experience.
+OpenAI [Whisper V3](https://huggingface.co/openai/whisper-large-v3) is used to produce a transcript, which is then passed into the [LLaMA 3.1 8B Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) language model to generate a response, which is then synthesized by Coqui's [XTTS](https://github.com/coqui-ai/TTS) text-to-speech model. All together, this produces a voice-to-voice chat experience.
 
 You can find the demo live [here](https://modal-labs--quillman-web.modal.run/).
 
@@ -23,32 +23,34 @@ You can find the demo live [here](https://modal-labs--quillman-web.modal.run/).
 ### Requirements
 
 - `modal` installed in your current Python virtual environment (`pip install modal`)
-- A [Modal](http://modal.com/) account
+- A [Modal](http://modal.com/) account (`modal setup`)
 - A Modal token set up in your environment (`modal token new`)
 
 ### Developing the inference modules
 
-Whisper, XTTS, and Llama each have a [`local_entrypoint`](https://modal.com/docs/reference/modal.App#local_entrypoint) method that is invoked when you run that file directly. 
+Whisper, XTTS, and Llama each have a [`local_entrypoint`](https://modal.com/docs/reference/modal.App#local_entrypoint)
+that is invoked when you execute the module with `modal run`.
 This is useful for testing each module standalone, without needing to run the whole app.
 
 For example, to test the Whisper transcription module, run:
+
 ```shell
 modal run -q src.whisper
 ```
 
 ### Developing the http server and frontend
 
-The http server at `src/app.py` is a [FastAPI](https://fastapi.tiangolo.com/) app that chains the inference modules into a single pipeline.
+The HTTP server at `src/app.py` is a [FastAPI](https://fastapi.tiangolo.com/) app that chains the inference modules into a single pipeline.
 
 It also serves the frontend as static files.
 
-To run a [development server]((https://modal.com/docs/guide/webhooks#developing-with-modal-serve)), execute this command from the root directory of this repo:
+To run a [development server](https://modal.com/docs/guide/webhooks#developing-with-modal-serve), execute this command from the root directory of this repo:
 
 ```shell
 modal serve src.app
 ```
 
-In the terminal output, you'll find a URL that you can visit to use your app. While the `modal serve` process is running, changes to any of the project files will be automatically applied. `Ctrl+C` will stop the app. Note that for frontend changes, the browser cache will need to be cleared.
+In the terminal output, you'll find a URL that you can visit to use your app. While the `modal serve` process is running, changes to any of the project files will be automatically applied. `Ctrl+C` will stop the app.
 
 ### Deploying to Modal