vLLM
/
vllm-project.github.io
mirror of https://github.com/vllm-project/vllm-project.github.io.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Update 2025-01-27-intro-to-llama-stack-with-vllm.md

This commit is contained in:
Yuan Tang 2025-01-24 16:51:39 -05:00 committed by GitHub
parent d2264ca838
commit 0121eb45e9
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
_posts/2025-01-27-intro-to-llama-stack-with-vllm.md
View File

@ -13,7 +13,7 @@ We are excited to announce that vLLM inference provider is now available in [Lla
Llama Stack defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations. Llama Stack defines and standardizes the set of core building blocks needed to bring generative AI applications to market. These building blocks are presented in the form of interoperable APIs with a broad set of Service Providers providing their implementations.
Llama Stack focuses on making it easy to build production applications with a variety of models - ranging from the latest Llama 3.3 model to specialized models like Llama Guard for safety. More models beyond the Llama model family are in the works. The goal is to provide pre-packaged implementations (aka “distributions”) which can be run in a variety of deployment environments. The Stack can assist you in your entire app development lifecycle - start iterating on local, mobile or desktop and seamlessly transition to on-prem or public cloud deployments. At every point in this transition, the same set of APIs and the same developer experience is available. Llama Stack focuses on making it easy to build production applications with a variety of models - ranging from the latest Llama 3.3 model to specialized models like Llama Guard for safety and other models. The goal is to provide pre-packaged implementations (aka “distributions”) which can be run in a variety of deployment environments. The Stack can assist you in your entire app development lifecycle - start iterating on local, mobile or desktop and seamlessly transition to on-prem or public cloud deployments. At every point in this transition, the same set of APIs and the same developer experience is available.
Each specific implementation of an API is called a "Provider" in this architecture. Users can swap providers via configuration. `vLLM` is a prominent example of a high-performance API backing the inference API. Each specific implementation of an API is called a "Provider" in this architecture. Users can swap providers via configuration. `vLLM` is a prominent example of a high-performance API backing the inference API.