What is the Hugging Face Hub and how do you push a trained model to share it?

The Hugging Face Hub is a platform hosting over 900,000 models, 200,000 datasets, and 300,000 Spaces (interactive apps). Every model on the Hub has a model card (README.md) documenting its architecture, training data, performance, intended uses, and limitations — following a community standard for responsible model sharing.

The huggingface_hub library and the push_to_hub method in Transformers make it trivial to upload models and interact with the Hub's API — browsing, downloading, and uploading models, datasets, and tokenizers.

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from huggingface_hub import HfApi, login

# Authenticate (or set HF_TOKEN env var)
login(token='hf_....')  # get token from huggingface.co/settings/tokens

# Load a fine-tuned local model and push to Hub
model     = AutoModelForSequenceClassification.from_pretrained('./my-model')
tokenizer = AutoTokenizer.from_pretrained('./my-model')

# Push to Hub (creates repo if it doesn't exist)
model.push_to_hub('your-username/my-sentiment-classifier')
tokenizer.push_to_hub('your-username/my-sentiment-classifier')

# ── Interact with Hub API directly
api = HfApi()

# List models by task or keyword
models = api.list_models(task='text-classification', sort='downloads', limit=5)
for m in models: print(m.modelId, m.downloads)

# Download a specific file from a repo
api.hf_hub_download(
    repo_id='bert-base-uncased',
    filename='config.json',
    local_dir='./downloaded'
)

# ── Create a Space (Gradio demo)
api.create_repo(
    repo_id='your-username/my-demo',
    repo_type='space',
    space_sdk='gradio',
)

# ── Quick inference with pipeline from Hub
from transformers import pipeline
clf = pipeline('text-classification', model='your-username/my-sentiment-classifier')
print(clf('This product is amazing!'))