Inference Endpoints offers out-of-the-box support for Machine Learning tasks from the following libraries:

Below is a table of Hugging Face managed supported tasks for Inference Endpoint. These tasks don’t require any form of code or “custom container” to deploy an Endpoint. If you want to customize any of the tasks below, or want to write your own custom task, check out the “Create your own inference handler” section for more information.

Most of the tasks below uses the pipeline object, and more information about what additional parameters can be sent to the endpoint is available here.

Task	Framework	Out of the box Support
Text To Image	Diffusers	✅
Text Classification	Transformers	✅
Zero Shot Classification	Transformers	✅
Token Classifiation	Transformers	✅
Question Answering	Transformers	✅
Fill Mask	Transformers	✅
Summarization	Transformers	✅
Translation	Transformers	✅
Text to Text Generation	Transformers	✅
Text Generation	Transformers	✅
Feature Extraction	Transformers	✅
Sentence Embeddings	Sentence Transformers	✅
Sentence Similarity	Sentence Transformers	✅
Ranking	Sentence Transformers	✅
Image Classification	Transformers	✅
Automatic Speech Recognition	Transformers	✅
Audio Classification	Transformers	✅
Object Detection	Transformers	✅
Image Segmentation	Transformers	✅
Table Question Answering	Transformers	✅
Conversational	Transformers	✅
Custom	Custom	✅
Visual Question Answering	Transformers	❌
Zero Shot Image Classification	Transformers	❌

Example Request payloads

Custom Handler

{
  "inputs": "This is a sample input",
  "moreData": 1,
  "customTask": true
}

Text Classification

Classifying a single text

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Classifying a text pair

{
  "inputs": {
    "text": "This sound track was beautiful!",
    "text_pair": "It paints the scenery in your mind so well I would recomend it even to people who hate vid. game music!"
  } 
}

Zero Shot Classification

{
  "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"]
  }
}

Token Classifiation

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Question Answering

{
  "inputs": {
    "question": "What is used for inference?",
    "context": "My Name is Philipp and I live in Nuremberg. This model is used with sagemaker for inference."
  }
}

Fill Mask

{
  "inputs": "This sound track was <mask>! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Summarization

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Translation

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Text to Text Generation

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Text Generation

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Feature Extraction

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Sentence Embeddings

{
  "inputs": "This sound track was beautiful! It paints the scenery in your mind so well I would recomend it
  even to people who hate vid. game music!"
}

Sentence similarity

{
  "inputs": {
    "sentences": ["This sound track was beautiful!", "It paints the scenery in your mind so well"],
    "source_sentence": "What a wonderful day to listen to music"
  }
}

Ranking

{
  "inputs": ["This sound track was beautiful!", "It paints the scenery in your mind so well"]
}

Image Classification

Image Classification can receive json payloads or binary data from a image directly.

{
  "inputs": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgI"
}

curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: image/jpg' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@test.jpg'

Automatic Speech Recognition

Automatic Speech Recognition can receive json payloads or binary data from a audio directly. For additional parameters, see this reference.

{
  "inputs": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgI"
}

curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: audio/x-flac' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@sample.flac'

Audio Classification

Audio Classification can receive json payloads or binary data from a audio directly. For additional parameters, see this reference.

{
  "inputs": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgI"
}

curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: audio/x-flac' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@sample.flac'

Object Detection

Object Detection can receive json payloads or binary data from a image directly. For additional parameters, see this reference.

{
  "inputs": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgI"
}

curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: image/jpg' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@test.jpg'

Image Segmentation

Image Segmentation can receive json payloads or binary data from a image directly. For additional parameters, see this reference.

{
  "inputs": "/9j/4AAQSkZJRgABAQEBLAEsAAD/2wBDAAMCAgI"
}

curl --request POST \
  --url https://{ENDPOINT}/ \
  --header 'Content-Type: image/jpg' \
  --header 'Authorization: Bearer {HF_TOKEN}' \
  --data-binary '@test.jpg'

Table Question Answering

{
  "inputs": {
    "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"]
    }
  }
}

Conversational

{"inputs": [
  {
      "role": "user",
      "content": "Which movie is the best ?"
  },
  {
      "role": "assistant",
      "content": "It's Die Hard for sure."
  },
  {
      "role": "user",
      "content": "Can you explain why?"
  }
]}

Text To Image

{        
  "inputs": "realistic render portrait realistic render portrait of group of flying blue whales towards the moon, intricate, toy, sci - fi, extremely detailed, digital painting, sculpted in zbrush, artstation, concept art, smooth, sharp focus, illustration, chiaroscuro lighting, golden ratio, incredible art by artgerm and greg rutkowski and alphonse mucha and simon stalenhag",
}

For text-to-image models, note that currently your model repo needs to be a diffusers model with the full weights in it (i.e., not just a LoRA).

Additional parameters

You can add additional parameters, which are supported by the pipelines api from transformers.

For Example if you have a text-generation pipeline you can provide generation_kwargs for repetition_penalty or max_length

{
  "inputs": "Hugging Face, the winner of VentureBeat’s Innovation in Natural Language Process/Understanding Award for 2021, is looking to level the playing field. The team, launched by Clément Delangue and Julien Chaumond in 2016, was recognized for its work in democratizing NLP, the global market value for which is expected to hit $35.1 billion by 2026. This week, Google’s former head of Ethical AI Margaret Mitchell joined the team.",
  "parameters": {
    "repetition_penalty": 4.0,
    "max_length": 128
  }
}

Supported Transformers & Diffusers Tasks