From d947ddd2554476eb3ee2d8e6e6505db7f4606cc5 Mon Sep 17 00:00:00 2001 From: Kelly Brown <86735520+kelbrown20@users.noreply.github.com> Date: Tue, 11 Feb 2025 09:53:26 -0500 Subject: [PATCH] docs: Updating wording and nits in the README.md (#992) # What does this PR do? Fixing some wording nits and added small formatting suggestions in the README.md ## Before submitting - [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Ran pre-commit to handle lint / formatting issues. - [x] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [x] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests. --- README.md | 33 +++++++++++++++++---------------- 1 file changed, 17 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index a5e5b217d..baec8c1bd 100644 --- a/README.md +++ b/README.md @@ -7,13 +7,13 @@ [**Quick Start**](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) | [**Documentation**](https://llama-stack.readthedocs.io/en/latest/index.html) | [**Colab Notebook**](./docs/getting_started.ipynb) -Llama Stack defines and standardizes the core building blocks that simplify AI application development. It codified best practices across the Llama ecosystem. More specifically, it provides +Llama Stack standardizes the core building blocks that simplify AI application development. It codifies best practices across the Llama ecosystem. More specifically, it provides - **Unified API layer** for Inference, RAG, Agents, Tools, Safety, Evals, and Telemetry. -- **Plugin architecture** to support the rich ecosystem of implementations of the different APIs in different environments like local development, on-premises, cloud, and mobile. -- **Prepackaged verified distributions** which offer a one-stop solution for developers to get started quickly and reliably in any environment -- **Multiple developer interfaces** like CLI and SDKs for Python, Typescript, iOS, and Android -- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack +- **Plugin architecture** to support the rich ecosystem of different API implementations in various environments, including local development, on-premises, cloud, and mobile. +- **Prepackaged verified distributions** which offer a one-stop solution for developers to get started quickly and reliably in any environment. +- **Multiple developer interfaces** like CLI and SDKs for Python, Typescript, iOS, and Android. +- **Standalone applications** as examples for how to build production-grade AI applications with Llama Stack.
### Llama Stack Benefits -- **Flexible Options**: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choice. -- **Consistent Experience**: With its unified APIs Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior. +- **Flexible Options**: Developers can choose their preferred infrastructure without changing APIs and enjoy flexible deployment choices. +- **Consistent Experience**: With its unified APIs, Llama Stack makes it easier to build, test, and deploy AI applications with consistent application behavior. - **Robust Ecosystem**: Llama Stack is already integrated with distribution partners (cloud providers, hardware vendors, and AI-focused companies) that offer tailored infrastructure, software, and services for deploying Llama models. By reducing friction and complexity, Llama Stack empowers developers to focus on what they do best: building transformative generative AI applications. ### API Providers -Here is a list of the various API providers and available distributions to developers started easily, +Here is a list of the various API providers and available distributions that can help developers get started easily with Llama Stack. | **API Provider Builder** | **Environments** | **Agents** | **Inference** | **Memory** | **Safety** | **Telemetry** | |:------------------------:|:----------------------:|:----------:|:-------------:|:----------:|:----------:|:-------------:| @@ -71,15 +71,15 @@ A Llama Stack Distribution (or "distro") is a pre-configured bundle of provider You have two ways to install this repository: -1. **Install as a package**: +* **Install as a package**: You can install the repository directly from [PyPI](https://pypi.org/project/llama-stack/) by running the following command: ```bash pip install llama-stack ``` -2. **Install from source**: +* **Install from source**: If you prefer to install from the source code, make sure you have [conda installed](https://docs.conda.io/projects/conda/en/stable). - Then, follow these steps: + Then, run the following commands: ```bash mkdir -p ~/local cd ~/local @@ -96,10 +96,11 @@ You have two ways to install this repository: Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest/index.html) page for more details. -* [CLI reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html) - * Guide using `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. -* [Getting Started](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html) - * Quick guide to start a Llama Stack server. +* CLI references + * [llama (server-side) CLI Reference](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/index.html): Guide for using the `llama` CLI to work with Llama models (download, study prompts), and building/starting a Llama Stack distribution. + * [llama (client-side) CLI Reference](https://llama-stack.readthedocs.io/en/latest/references/llama_stack_client_cli_reference.html): Guide for using the `llama-stack-client` CLI, which allows you to query information about the distribution. +* Getting Started + * [Quick guide to start a Llama Stack server](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html). * [Jupyter notebook](./docs/getting_started.ipynb) to walk-through how to use simple text and vision inference llama_stack_client APIs * The complete Llama Stack lesson [Colab notebook](https://colab.research.google.com/drive/1dtVmxotBsI4cGZQNsJRYPrLiDeT0Wnwt) of the new [Llama 3.2 course on Deeplearning.ai](https://learn.deeplearning.ai/courses/introducing-multimodal-llama-3-2/lesson/8/llama-stack). * A [Zero-to-Hero Guide](https://github.com/meta-llama/llama-stack/tree/main/docs/zero_to_hero_guide) that guide you through all the key components of llama stack with code samples. @@ -115,6 +116,6 @@ Please checkout our [Documentation](https://llama-stack.readthedocs.io/en/latest | Typescript | [llama-stack-client-typescript](https://github.com/meta-llama/llama-stack-client-typescript) | [![NPM version](https://img.shields.io/npm/v/llama-stack-client.svg)](https://npmjs.org/package/llama-stack-client) | Kotlin | [llama-stack-client-kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) | [![Maven version](https://img.shields.io/maven-central/v/com.llama.llamastack/llama-stack-client-kotlin)](https://central.sonatype.com/artifact/com.llama.llamastack/llama-stack-client-kotlin) -Check out our client SDKs for connecting to Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [typescript](https://github.com/meta-llama/llama-stack-client-typescript), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications. +Check out our client SDKs for connecting to a Llama Stack server in your preferred language, you can choose from [python](https://github.com/meta-llama/llama-stack-client-python), [typescript](https://github.com/meta-llama/llama-stack-client-typescript), [swift](https://github.com/meta-llama/llama-stack-client-swift), and [kotlin](https://github.com/meta-llama/llama-stack-client-kotlin) programming languages to quickly build your applications. You can find more example scripts with client SDKs to talk with the Llama Stack server in our [llama-stack-apps](https://github.com/meta-llama/llama-stack-apps/tree/main/examples) repo.