#
Run Inference on servers

Inference is the process of using a trained model to make predictions on new data. Because this process can be compute-intensive, running on a dedicated or external service can be an interesting option. The `huggingface_hub` library provides a unified interface to run inference across multiple services for models hosted on the Hugging Face Hub:

1. HF Inference API: a serverless solution that allows you to run model inference on Hugging Face’s infrastructure for free. This service is a fast way to get started, test different models, and prototype AI products.
2. Third-party providers: various serverless solution provided by external providers (Together, Sambanova, etc.). These providers offer production-ready APIs on a pay-as-you-go model. This is the fastest way to integrate AI in your products with a maintenance-free and scalable solution. Refer to the Supported providers and tasks section for a list of supported providers.
3. Inference Endpoints: a product to easily deploy models to production. Inference is run by Hugging Face in a dedicated, fully managed infrastructure on a cloud provider of your choice.

These services can all be called from the InferenceClient object. It acts as a replacement for the legacy InferenceApi client, adding specific support for tasks and third-party providers. Learn how to migrate to the new client in the Legacy InferenceAPI client section.

InferenceClient is a Python client making HTTP calls to our APIs. If you want to make the HTTP calls directly using your preferred tool (curl, postman,…), please refer to the Inference API or to the Inference Endpoints documentation pages.

For web development, a JS client has been released. If you are interested in game development, you might have a look at our C# project.

##
Getting started

Let’s get started with a text-to-image task:

    >>> from huggingface_hub import InferenceClient

    # Example with an external provider (e.g. replicate)
    >>> replicate_client = InferenceClient(
        provider="replicate",
        api_key="my_replicate_api_key",
    )
    >>> replicate_image = replicate_client.text_to_image(
        "A flying car crossing a futuristic cityscape.",
        model="black-forest-labs/FLUX.1-schnell",
    )
    >>> replicate_image.save("flying_car.png")

In the example above, we initialized an InferenceClient with a third-party provider, Replicate. When using a provider, you must specify the model you want to use. The model id must be the id of the model on the Hugging Face Hub, not the id of the model from the third-party provider. In our example, we generated an image from a text prompt. The returned value is a `PIL.Image` object that can be saved to a file. For more details, check out the text_to_image() documentation.

Let’s now see an example using the chat_completion() API. This task uses an LLM to generate a response from a list of messages:

    >>> from huggingface_hub import InferenceClient
    >>> messages = [
        {
            "role": "user",
            "content": "What is the capital of France?",
        }
    ]
    >>> client = InferenceClient(
        provider="together",
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        api_key="my_together_api_key",
    )
    >>> client.chat_completion(messages, max_tokens=100)
    ChatCompletionOutput(
        choices=[
            ChatCompletionOutputComplete(
                finish_reason="eos_token",
                index=0,
                message=ChatCompletionOutputMessage(
                    role="assistant", content="The capital of France is Paris.", name=None, tool_calls=None
                ),
                logprobs=None,
            )
        ],
        created=1719907176,
        id="",
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        object="text_completion",
        system_fingerprint="2.0.4-sha-f426a33",
        usage=ChatCompletionOutputUsage(completion_tokens=8, prompt_tokens=17, total_tokens=25),
    )

In the example above, we used a third-party provider (Together AI) and specified which model we want to use (`"meta-llama/Meta-Llama-3-8B-Instruct"`). We then gave a list of messages to complete (here, a single question) and passed an additional parameter to the API (`max_token=100`). The output is a `ChatCompletionOutput` object that follows the OpenAI specification. The generated content can be accessed with `output.choices[0].message.content`. For more details, check out the chat_completion() documentation.

The API is designed to be simple. Not all parameters and options are available or described for the end user. Check out this page if you are interested in learning more about all the parameters available for each task.

###
Using a specific provider

If you want to use a specific provider, you can specify it when initializing the client. The default provider is “hf-inference”, the Hugging Face Serverless Inference API. Refer to the Supported providers and tasks section for a list of supported providers.

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient(provider="replicate", api_key="my_replicate_api_key")

###
Using a specific model

What if you want to use a specific model? You can specify it either as a parameter or directly at an instance level:

    >>> from huggingface_hub import InferenceClient
    # Initialize client for a specific model
    >>> client = InferenceClient(model="prompthero/openjourney-v4")
    >>> client.text_to_image(...)
    # Or use a generic client but pass your model as an argument
    >>> client = InferenceClient()
    >>> client.text_to_image(..., model="prompthero/openjourney-v4")

When using the Hugging Face Inference API (default provider), each task comes with a recommended model from the 200k+ models available on the Hub. However, this recommendation can change over time, so it’s best to explicitly set a model once you’ve decided which one to use. For third-party providers, you must always specify a model that is compatible with that provider.

Visit the Models page on the Hub to explore models available through the Inference API, or check the provider’s documentation for their supported models.

###
Using a specific URL

The examples we saw above use either the Hugging Face Inference API or third-party providers. While these prove to be very useful for prototyping and testing things quickly. Once you’re ready to deploy your model to production, you’ll need to use a dedicated infrastructure. That’s where Inference Endpoints comes into play. It allows you to deploy any model and expose it as a private API. Once deployed, you’ll get a URL that you can connect to using exactly the same code as before, changing only the `model` parameter:

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient(model="https://uu149rez6gw9ehej.eu-west-1.aws.endpoints.huggingface.cloud/deepfloyd-if")
    # or
    >>> client = InferenceClient()
    >>> client.text_to_image(..., model="https://uu149rez6gw9ehej.eu-west-1.aws.endpoints.huggingface.cloud/deepfloyd-if")

Note that you cannot specify both a URL and a provider - they are mutually exclusive. URLs are used to connect directly to deployed endpoints.

###
Authentication

Authentication depends on which provider you are using:

1. For the default Hugging Face Inference API, you can authenticate using a User Access Token:

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient(token="hf_***")

By default, it will use the token saved on your machine if you are logged in (see how to authenticate).

2. For third-party providers, you have two options:

Direct access to provider: Use your own API key to interact directly with the provider’s service:

    >>> client = InferenceClient(
        provider="replicate",
        api_key="r8_****"  # Your Replicate API key
    )

Routed through Hugging Face : Use Hugging Face as a proxy to access third-party providers. Simply specify your Hugging Face token and the provider you want to use. The calls will be routed through Hugging Face’s infrastructure using our provider keys, and the usage will be billed directly to your Hugging Face account:

    >>> client = InferenceClient(
        provider="replicate",
        token="hf_****"  # Your HF token
    )

##
OpenAI compatibility

The `chat_completion` task follows OpenAI’s Python client syntax. What does it mean for you? It means that if you are used to play with `OpenAI`’s APIs you will be able to switch to `huggingface_hub.InferenceClient` to work with open-source models by updating just 2 line of code!

    - from openai import OpenAI
    + from huggingface_hub import InferenceClient

    - client = OpenAI(
    + client = InferenceClient(
        base_url=...,
        api_key=...,
    )

    output = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Count to 10"},
        ],
        stream=True,
        max_tokens=1024,
    )

    for chunk in output:
        print(chunk.choices[0].delta.content)

And that’s it! The only required changes are to replace `from openai import OpenAI` by `from huggingface_hub import InferenceClient` and `client = OpenAI(...)` by `client = InferenceClient(...)`. You can choose any LLM model from the Hugging Face Hub by passing its model id as `model` parameter. Here is a list of supported models. For authentication, you should pass a valid User Access Token as `api_key` or authenticate using `huggingface_hub` (see the authentication guide).

All input parameters and output format are strictly the same. In particular, you can pass `stream=True` to receive tokens as they are generated. You can also use the AsyncInferenceClient to run inference using `asyncio`:

    import asyncio
    - from openai import AsyncOpenAI
    + from huggingface_hub import AsyncInferenceClient

    - client = AsyncOpenAI()
    + client = AsyncInferenceClient()

    async def main():
        stream = await client.chat.completions.create(
            model="meta-llama/Meta-Llama-3-8B-Instruct",
            messages=[{"role": "user", "content": "Say this is a test"}],
            stream=True,
        )
        async for chunk in stream:
            print(chunk.choices[0].delta.content or "", end="")

    asyncio.run(main())

You might wonder why using InferenceClient instead of OpenAI’s client? There are a few reasons for that:

1. InferenceClient is configured for Hugging Face services. You don’t need to provide a `base_url` to run models on the serverless Inference API. You also don’t need to provide a `token` or `api_key` if your machine is already correctly logged in.
2. InferenceClient is tailored for both Text-Generation-Inference (TGI) and `transformers` frameworks, meaning you are assured it will always be on-par with the latest updates.
3. InferenceClient is integrated with our Inference Endpoints service, making it easier to launch an Inference Endpoint, check its status and run inference on it. Check out the Inference Endpoints guide for more details.

`InferenceClient.chat.completions.create` is simply an alias for `InferenceClient.chat_completion`. Check out the package reference of chat_completion() for more details. `base_url` and `api_key` parameters when instantiating the client are also aliases for `model` and `token`. These aliases have been defined to reduce friction when switching from `OpenAI` to `InferenceClient`.

##
Supported providers and tasks

InferenceClient’s goal is to provide the easiest interface to run inference on Hugging Face models, on any provider. It has a simple API that supports the most common tasks. Here is a table showing which providers support which tasks:

Domain | Task | Black Forest Labs | Cerebras | Cohere | fal-ai | Fireworks AI | HF Inference | Hyperbolic | Nebius AI Studio | Novita AI | Replicate | Sambanova | Together
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
Audio | audio_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| audio_to_audio() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| automatic_speech_recognition() | ❌ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| text_to_speech() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌
Computer Vision | image_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| image_segmentation() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| image_to_image() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| image_to_text() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| object_detection() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| text_to_image() | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅
| text_to_video() | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌
| zero_shot_image_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
Multimodal | document_question_answering() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| visual_question_answering() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
NLP | chat_completion() | ❌ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅
| feature_extraction() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| fill_mask() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| question_answering() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| sentence_similarity() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| summarization() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| table_question_answering() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| text_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| text_generation() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅
| token_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| translation() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| zero_shot_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
Tabular | tabular_classification() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌
| tabular_regression() | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌

Check out the Tasks page to learn more about each task.

##
Custom requests

However, it is not always possible to cover all use cases. For custom requests, the InferenceClient.post() method gives you the flexibility to send any request to the Inference API. For example, you can specify how to parse the inputs and outputs. In the example below, the generated image is returned as raw bytes instead of parsing it as a `PIL Image`. This can be helpful if you don’t have `Pillow` installed in your setup and just care about the binary content of the image. InferenceClient.post() is also useful to handle tasks that are not yet officially supported.

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient()
    >>> response = client.post(json={"inputs": "An astronaut riding a horse on the moon."}, model="stabilityai/stable-diffusion-2-1")
    >>> response.content # raw bytes
    b'...'

##
Async client

An async version of the client is also provided, based on `asyncio` and `aiohttp`. You can either install `aiohttp` directly or use the `[inference]` extra:

    pip install --upgrade huggingface_hub[inference]
    # or
    # pip install aiohttp

After installation all async API endpoints are available via AsyncInferenceClient. Its initialization and APIs are strictly the same as the sync-only version.

    # Code must be run in an asyncio concurrent context.
    # $ python -m asyncio
    >>> from huggingface_hub import AsyncInferenceClient
    >>> client = AsyncInferenceClient()

    >>> image = await client.text_to_image("An astronaut riding a horse on the moon.")
    >>> image.save("astronaut.png")

    >>> async for token in await client.text_generation("The Huggingface Hub is", stream=True):
    ...     print(token, end="")
     a platform for sharing and discussing ML-related content.

For more information about the `asyncio` module, please refer to the official documentation.

##
Advanced tips

In the above section, we saw the main aspects of InferenceClient. Let’s dive into some more advanced tips.

###
Timeout

When doing inference, there are two main causes for a timeout:

- The inference process takes a long time to complete.
- The model is not available, for example when Inference API is loading it for the first time.

InferenceClient has a global `timeout` parameter to handle those two aspects. By default, it is set to `None`, meaning that the client will wait indefinitely for the inference to complete. If you want more control in your workflow, you can set it to a specific value in seconds. If the timeout delay expires, an InferenceTimeoutError is raised. You can catch it and handle it in your code:

    >>> from huggingface_hub import InferenceClient, InferenceTimeoutError
    >>> client = InferenceClient(timeout=30)
    >>> try:
    ...     client.text_to_image(...)
    ... except InferenceTimeoutError:
    ...     print("Inference timed out after 30s.")

###
Binary inputs

Some tasks require binary inputs, for example, when dealing with images or audio files. In this case, InferenceClient tries to be as permissive as possible and accept different types:

- raw `bytes`
- a file-like object, opened as binary (`with open("audio.flac", "rb") as f: ...`)
- a path (`str` or `Path`) pointing to a local file
- a URL (`str`) pointing to a remote file (e.g. `https://...`). In this case, the file will be downloaded locally before sending it to the Inference API.

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient()
    >>> client.image_classification("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg")
    [{'score': 0.9779096841812134, 'label': 'Blenheim spaniel'}, ...]

##
Legacy InferenceAPI client

InferenceClient acts as a replacement for the legacy InferenceApi client. It adds specific support for tasks and handles inference on both Inference API and Inference Endpoints.

Here is a short guide to help you migrate from InferenceApi to InferenceClient.

###
Initialization

Change from

    >>> from huggingface_hub import InferenceApi
    >>> inference = InferenceApi(repo_id="bert-base-uncased", token=API_TOKEN)

to

    >>> from huggingface_hub import InferenceClient
    >>> inference = InferenceClient(model="bert-base-uncased", token=API_TOKEN)

###
Run on a specific task

Change from

    >>> from huggingface_hub import InferenceApi
    >>> inference = InferenceApi(repo_id="paraphrase-xlm-r-multilingual-v1", task="feature-extraction")
    >>> inference(...)

to

    >>> from huggingface_hub import InferenceClient
    >>> inference = InferenceClient()
    >>> inference.feature_extraction(..., model="paraphrase-xlm-r-multilingual-v1")

This is the recommended way to adapt your code to InferenceClient. It lets you benefit from the task-specific methods like `feature_extraction`.

###
Run custom request

Change from

    >>> from huggingface_hub import InferenceApi
    >>> inference = InferenceApi(repo_id="bert-base-uncased")
    >>> inference(inputs="The goal of life is [MASK].")
    [{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]

to

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient()
    >>> response = client.post(json={"inputs": "The goal of life is [MASK]."}, model="bert-base-uncased")
    >>> response.json()
    [{'sequence': 'the goal of life is life.', 'score': 0.10933292657136917, 'token': 2166, 'token_str': 'life'}]

###
Run with parameters

Change from

    >>> from huggingface_hub import InferenceApi
    >>> inference = InferenceApi(repo_id="typeform/distilbert-base-uncased-mnli")
    >>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
    >>> params = {"candidate_labels":["refund", "legal", "faq"]}
    >>> inference(inputs, params)
    {'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}

to

    >>> from huggingface_hub import InferenceClient
    >>> client = InferenceClient()
    >>> inputs = "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!"
    >>> params = {"candidate_labels":["refund", "legal", "faq"]}
    >>> response = client.post(json={"inputs": inputs, "parameters": params}, model="typeform/distilbert-base-uncased-mnli")
    >>> response.json()
    {'sequence': 'Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!', 'labels': ['refund', 'faq', 'legal'], 'scores': [0.9378499388694763, 0.04914155602455139, 0.013008488342165947]}

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