Merge pull request #24095 from ericcurtin/hf-doc

Add Hugging Face support to Docker Model Runner docs

Author: dvdksn

content/manuals/ai/model-runner/_index.md

@@ -17,8 +17,8 @@ aliases:
 Docker Model Runner (DMR) makes it easy to manage, run, and
 deploy AI models using Docker. Designed for developers,
 Docker Model Runner streamlines the process of pulling, running, and serving
-large language models (LLMs) and other AI models directly from Docker Hub or any
-OCI-compliant registry.
+large language models (LLMs) and other AI models directly from Docker Hub,
+any OCI-compliant registry, or [Hugging Face](https://huggingface.co/).
 
 With seamless integration into Docker Desktop and Docker
 Engine, you can serve models via OpenAI and Ollama-compatible APIs, package GGUF files as
@@ -32,7 +32,8 @@ with AI models locally.
 
 ## Key features
 
-- [Pull and push models to and from Docker Hub](https://hub.docker.com/u/ai)
+- [Pull and push models to and from Docker Hub or any OCI-compliant registry](https://hub.docker.com/u/ai)
+- [Pull models from Hugging Face](https://huggingface.co/)
 - Serve models on [OpenAI and Ollama-compatible APIs](api-reference.md) for easy integration with existing apps
 - Support for [llama.cpp, vLLM, and Diffusers inference engines](inference-engines.md) (vLLM and Diffusers on Linux with NVIDIA GPUs)
 - [Generate images from text prompts](inference-engines.md#diffusers) using Stable Diffusion models with the Diffusers backend
@@ -81,11 +82,12 @@ Docker Engine only:
 
 ## How Docker Model Runner works
 
-Models are pulled from Docker Hub the first time you use them and are stored
-locally. They load into memory only at runtime when a request is made, and
-unload when not in use to optimize resources. Because models can be large, the
-initial pull may take some time. After that, they're cached locally for faster
-access. You can interact with the model using
+Models are pulled from Docker Hub, an OCI-compliant registry, or
+[Hugging Face](https://huggingface.co/) the first time you use them and are
+stored locally. They load into memory only at runtime when a request is made,
+and unload when not in use to optimize resources. Because models can be large,
+the initial pull may take some time. After that, they're cached locally for
+faster access. You can interact with the model using
 [OpenAI and Ollama-compatible APIs](api-reference.md).
 
 ### Inference engines