#
HF Inference

> All supported HF Inference models can be found here

HF Inference is the serverless Inference API powered by Hugging Face. This service used to be called “Inference API (serverless)” prior to Inference Providers. If you are interested in deploying models to a dedicated and autoscaling infrastructure managed by Hugging Face, check out Inference Endpoints instead.

As of July 2025, hf-inference focuses mostly on CPU inference (e.g. embedding, text-ranking, text-classification, or smaller LLMs that have historical importance like BERT or GPT-2).

##
Supported tasks

###
Automatic Speech Recognition

Find out more about Automatic Speech Recognition here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    output = client.automatic_speech_recognition("sample1.flac", model="openai/whisper-large-v3")

###
Chat Completion (LLM)

Find out more about Chat Completion (LLM) here.

Language

Client

Provider

Settings

    import os
    from openai import OpenAI

    client = OpenAI(
        base_url="https://router.huggingface.co/v1",
        api_key=os.environ["HF_TOKEN"],
    )

    completion = client.chat.completions.create(
        model="katanemo/Arch-Router-1.5B:hf-inference",
        messages=[
            {
                "role": "user",
                "content": "What is the capital of France?"
            }
        ],
    )

    print(completion.choices[0].message)

###
Feature Extraction

Find out more about Feature Extraction here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    result = client.feature_extraction(
        "Today is a sunny day and I will get some ice cream.",
        model="intfloat/multilingual-e5-large",
    )

###
Fill Mask

Find out more about Fill Mask here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    result = client.fill_mask(
        "The answer to the universe is undefined.",
        model="google-bert/bert-base-uncased",
    )

###
Image Classification

Find out more about Image Classification here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    output = client.image_classification("cats.jpg", model="Falconsai/nsfw_image_detection")

###
Image Segmentation

Find out more about Image Segmentation here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    output = client.image_segmentation("cats.jpg", model="mattmdjaga/segformer_b2_clothes")

###
Object Detection

Find out more about Object Detection here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    output = client.object_detection("cats.jpg", model="facebook/detr-resnet-50")

###
Question Answering

Find out more about Question Answering here.

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    answer = client.question_answering(
        question="What is my name?",
        context="My name is Clara and I live in Berkeley.",
        model="deepset/roberta-base-squad2",
    )

###
Summarization

Find out more about Summarization here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    result = client.summarization(
        "The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.",
        model="facebook/bart-large-cnn",
    )

###
Table Question Answering

Find out more about Table Question Answering here.

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    answer = client.table_question_answering(
        query="How many stars does the transformers repository have?",
        table={"Repository":["Transformers","Datasets","Tokenizers"],"Stars":["36542","4512","3934"],"Contributors":["651","77","34"],"Programming language":["Python","Python","Rust, Python and NodeJS"]},
        model="google/tapas-base-finetuned-wtq",
    )

###
Text Classification

Find out more about Text Classification here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    result = client.text_classification(
        "I like you. I love you",
        model="ProsusAI/finbert",
    )

###
Text Generation

Find out more about Text Generation here.

Language

Client

Provider

Settings

    import os
    from openai import OpenAI

    client = OpenAI(
        base_url="https://router.huggingface.co/hf-inference/models/katanemo/Arch-Router-1.5B",
        api_key=os.environ["HF_TOKEN"],
    )

    completion = client.chat.completions.create(
        model="katanemo/Arch-Router-1.5B",
        messages="\"Can you please let us know more details about your \"",
    )

    print(completion.choices[0].message)

###
Text To Image

Find out more about Text To Image here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    # output is a PIL.Image object
    image = client.text_to_image(
        "Astronaut riding a horse",
        model="black-forest-labs/FLUX.1-dev",
    )

###
Token Classification

Find out more about Token Classification here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    result = client.token_classification(
        "My name is Sarah Jessica Parker but you can call me Jessica",
        model="dslim/bert-base-NER",
    )

###
Translation

Find out more about Translation here.

Language

Client

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="hf-inference",
        api_key=os.environ["HF_TOKEN"],
    )

    result = client.translation(
        "Меня зовут Вольфганг и я живу в Берлине",
        model="google-t5/t5-small",
    )

###
Zero Shot Classification

Find out more about Zero Shot Classification here.

Language

Provider

Settings

    import os
    import requests

    API_URL = "https://router.huggingface.co/hf-inference/models/facebook/bart-large-mnli"
    headers = {
        "Authorization": f"Bearer {os.environ['HF_TOKEN']}",
    }

    def query(payload):
        response = requests.post(API_URL, headers=headers, json=payload)
        return response.json()

    output = query({
        "inputs": "Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!",
        "parameters": {"candidate_labels": ["refund", "legal", "faq"]},
    })

Update on GitHub