[docs] Fix misc typos and formatting issues in intro docs

Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>

docs/source/distributions/selection.md

@@ -23,7 +23,7 @@ Which templates / distributions to choose depends on the hardware you have for r
   - {dockerhub}`distribution-together` ([Guide](self_hosted_distro/together))
   - {dockerhub}`distribution-fireworks` ([Guide](self_hosted_distro/fireworks))
 
-- **Do you want to run Llama Stack inference on your iOS / Android device**  Lastly, we also provide templates for running Llama Stack inference on your iOS / Android device:
+- **Do you want to run Llama Stack inference on your iOS / Android device?**  Lastly, we also provide templates for running Llama Stack inference on your iOS / Android device:
   - [iOS SDK](ondevice_distro/ios_sdk)
   - [Android](ondevice_distro/android_sdk)
 

docs/source/distributions/self_hosted_distro/ollama.md

@@ -25,7 +25,9 @@ The `llamastack/distribution-ollama` distribution consists of the following prov
 | vector_io | `inline::faiss`, `remote::chromadb`, `remote::pgvector` |
 
 
-You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.### Environment Variables
+You should use this distribution if you have a regular desktop machine without very powerful GPUs. Of course, if you have powerful GPUs, you can still continue using this distribution since Ollama supports GPU acceleration.
+
+### Environment Variables
 
 The following environment variables can be configured:
 

docs/source/getting_started/index.md

@@ -1,6 +1,6 @@
 # Quick Start
 
-In this guide, we'll walk through how you can use the Llama Stack (server and client SDK ) to test a simple RAG agent.
+In this guide, we'll walk through how you can use the Llama Stack (server and client SDK) to test a simple RAG agent.
 
 A Llama Stack agent is a simple integrated system that can perform tasks by combining a Llama model for reasoning with tools (e.g., RAG, web search, code execution, etc.) for taking actions.