Expert Guide: Set Up Hugging Face Hub in 5 Steps

hf_abcdefghijklmnopqrstuvwxyz1234567890

# Save to a file (Linux/macOS)
echo "hf_abcdefghijklmnopqrstuvwxyz1234567890" > ~/.huggingface/token

# Set permissions (Linux/macOS)
chmod 600 ~/.huggingface/token

pip install --upgrade "huggingface_hub[cli]"

huggingface-cli login

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To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens.
Token:

ValueError: Token is invalid. Please try again.

from huggingface_hub import snapshot_download

# Download a model (e.g., "bert-base-uncased")
model_path = snapshot_download(
    repo_id="bert-base-uncased",
    local_dir="./bert-base-uncased",
    local_dir_use_symlinks=False  # Avoid symlinks for portability
)

print(f"Model downloaded to: {model_path}")

Model downloaded to: ./bert-base-uncased

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModel.from_pretrained("bert-base-uncased")

inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model(**inputs)
print(outputs.last_hidden_state.shape)

torch.Size([1, 4, 768])

import requests

API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-v0.1"
headers = {"Authorization": "Bearer hf_abcdefghijklmnopqrstuvwxyz1234567890"}

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

output = query({
    "inputs": "Explain AI to a 5-year-old:",
    "parameters": {"max_new_tokens": 50}
})

print(output[0]["generated_text"])

AI is like a super-smart robot friend that learns from books and games to help us with fun stuff, like drawing or telling stories!

import gradio as gr

def greet(name):
    return f"Hello {name}!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch()

# Create a new repository
huggingface-cli repo create my-awesome-model --type model

# Clone the repo
git lfs install
git clone https://huggingface.co/your-username/my-awesome-model
cd my-awesome-model

# Add your model files (e.g., PyTorch weights)
cp /path/to/your/model/* .

# Commit and push
git add .
git commit -m "Add my awesome model"
git push

from huggingface_hub import HfApi

api = HfApi()
api.upload_file(
    path_or_fileobj="/path/to/pytorch_model.bin",
    path_in_repo="pytorch_model.bin",
    repo_id="your-username/my-awesome-model",
    repo_type="model",
)

from huggingface_hub import upload_file

upload_file(
    path_or_fileobj="/path/to/large-model.bin",
    path_in_repo="large-model.bin",
    repo_id="your-username/my-awesome-model",
    repo_type="model",
    chunk_size=10 * 1024 * 1024,  # 10MB chunks
)

pip install --upgrade transformers

from transformers import pipeline

classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
result = classifier("I love Hugging Face Hub!")
print(result)

[{'label': 'POSITIVE', 'score': 0.9998}]

from transformers import pipeline

classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
result = classifier("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cat.png")
print(result)

[{'label': 'tabby cat', 'score': 0.98}]

pip install torch tensorflow  # For PyTorch/TensorFlow models