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dvdksn merged 1 commit into from
Feb 6, 2026
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Add Hugging Face support to Docker Model Runner docs #24095

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18 changes: 10 additions & 8 deletions content/manuals/ai/model-runner/_index.md
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down