AI & ML2025-01-24

Hugging Face Inference API

Access thousands of open-source models for NLP, computer vision, and audio tasks via simple API calls.

https://huggingface.co/docs/api-inference

Hugging Face Inference API

Hugging Face provides free and paid access to thousands of state-of-the-art machine learning models through their Inference API.

Key Features

Model Categories

  • NLP: Text classification, named entity recognition, question answering
  • Computer Vision: Image classification, object detection, segmentation
  • Audio: Speech recognition, audio classification
  • Multimodal: Image-to-text, visual question answering

Advantages

  • No infrastructure setup required
  • Access to 100,000+ models
  • Free tier available
  • Easy to switch between models
  • Low latency inference

Available Models

Popular NLP Models

  • BERT, RoBERTa for classification
  • GPT-2, GPT-Neo for generation
  • T5, BART for summarization
  • sentence-transformers for embeddings

Vision Models

  • CLIP for image understanding
  • YOLO for object detection
  • Stable Diffusion for image generation
  • SAM for segmentation

Pricing

  • Free Tier: Rate-limited access to most models
  • Pro ($9/month): Higher rate limits, faster inference
  • Enterprise: Dedicated endpoints with SLAs

Getting Started

import requests

API_URL = "https://api-inference.huggingface.co/models/bert-base-uncased"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

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

output = query({
    "inputs": "The answer to the universe is [MASK].",
})
print(output)

Using the Python Library

from huggingface_hub import InferenceClient

client = InferenceClient(token="your-token")

# Text generation
result = client.text_generation(
    "Once upon a time",
    model="gpt2"
)

# Image classification
with open("image.jpg", "rb") as f:
    result = client.image_classification(f)

Use Cases

Research & Experimentation

  • Quickly test different models
  • Compare model performance
  • Prototype ML applications

Production Applications

  • Sentiment analysis at scale
  • Document classification
  • Image moderation
  • Speech transcription

Best Practices

  1. Model Selection: Choose the right model for your task
  2. Caching: Cache results for repeated queries
  3. Batch Processing: Send multiple inputs when possible
  4. Error Handling: Handle rate limits gracefully
  5. Model Cards: Read model documentation before use

Advanced Features

Custom Endpoints

Deploy your own models with dedicated infrastructure:

  • Guaranteed uptime
  • Autoscaling
  • Custom hardware (GPU/CPU)

Inference Endpoints

from huggingface_hub import InferenceEndpoint

endpoint = InferenceEndpoint(
    "my-endpoint-name",
    token="your-token"
)

result = endpoint({"inputs": "Hello world"})

Resources