Inference Providers

Please refer to the Inference Providers Documentation for detailed information.

What technology do you use to power the HF-Inference API?

For 🤗 Transformers models, Pipelines power the API.

On top of Pipelines and depending on the model type, there are several production optimizations like:

• compiling models to optimized intermediary representations (e.g. ONNX),
• maintaining a Least Recently Used cache, ensuring that the most popular models are always loaded,
• scaling the underlying compute infrastructure on the fly depending on the load constraints.

For models from other libraries, the API uses Starlette and runs in Docker containers. Each library defines the implementation of different pipelines.

How can I turn off the HF-Inference API for my model?

Specify inference: false in your model card’s metadata.

Why don’t I see an inference widget, or why can’t I use the API?

For some tasks, there might not be support in the HF-Inference API, and, hence, there is no widget. For all libraries (except 🤗 Transformers), there is a library-to-tasks.ts file of supported tasks in the API. When a model repository has a task that is not supported by the repository library, the repository has inference: false by default.

Can I send large volumes of requests? Can I get accelerated APIs?

If you are interested in accelerated inference, higher volumes of requests, or an SLA, please contact us at api-enterprise at huggingface.co.

How can I see my usage?

You can check your usage in the Inference Dashboard. The dashboard shows both your serverless and dedicated endpoints usage.

Is there programmatic access to the HF-Inference API?

Yes, the huggingface_hub library has a client wrapper documented here.