Update README, add newline between API surface configurations

README.md

@@ -3,9 +3,42 @@
 [![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-toolchain)](https://pypi.org/project/llama-toolchain/)
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-This repo contains the API specifications for various components of the Llama Stack as well implementations for some of those APIs like model inference.
+This repository contains the API specifications and implementations for various components of the Llama Stack.
+
+The [Llama Stack](https://github.com/meta-llama/llama-toolchain/pull/8) defines and standardizes the building blocks needed to bring generative AI applications to market. These blocks span the entire development lifecycle: from model training and fine-tuning, through product evaluation, to invoking AI agents in production. Beyond definition, we're developing open-source versions and partnering with cloud providers, ensuring developers can assemble AI solutions using consistent, interlocking pieces across platforms. The ultimate goal is to accelerate innovation in the AI space.
+
+The Stack APIs are rapidly improving, but still very much Works in Progress and we invite feedback as well as direct contributions.
+
+
+## APIs
+
+The Llama Stack consists of the following set of APIs:
+
+- Inference
+- Safety
+- Memory
+- Agentic System
+- Evaluation
+- Post Training
+- Synthetic Data Generation
+- Reward Scoring
+
+Each of the APIs themselves is a collection of REST endpoints.
+
+
+## API Providers
+
+A Provider is what makes the API real -- they provide the actual implementation backing the API.
+
+As an example, for Inference, we could have the implementation be backed by primitives from `[ torch | vLLM | TensorRT ]` as possible options.
+
+A provider can also be just a pointer to a remote REST service -- for example, cloud providers like `[ aws | gcp ]` could possibly serve these APIs.
+
+
+## Llama Stack Distribution
+
+A Distribution is where APIs and Providers are assembled together to provide a consistent whole to the end application developer. You can mix-and-match providers -- some could be backed by inline code and some could be remote. As a hobbyist, you can serve a small model locally, but can choose a cloud provider for a large model. Regardless, the higher level APIs your app needs to work with don't need to change at all. You can even imagine moving across the server / mobile-device boundary as well always using the same uniform set of APIs for developing Generative AI applications.
 
-The Llama Stack consists of toolchain-apis and agentic-apis. This repo contains the toolchain-apis.
 
 ## Installation
 
@@ -27,4 +60,4 @@ pip install -e .
 
 ## The Llama CLI
 
-The `llama` CLI makes it easy to configure and run the Llama toolchain. Read the [CLI reference](docs/cli_reference.md) for details.
+The `llama` CLI makes it easy to work with the Llama Stack set of tools, including installing and running Distributions, downloading models, studying model prompt formats, etc. Please see the [CLI reference](docs/cli_reference.md) for details.

llama_toolchain/cli/distribution/configure.py

@@ -91,6 +91,7 @@ def configure_llama_distribution(dist: "Distribution", config: "DistributionConf
                 else None
             ),
         )
+        print("")
 
         config.providers[api.value] = {
             "provider_id": provider_spec.provider_id,