HuggingFace Guide: From Downloading Models to Publishing Your Own

Every tutorial tells you to "go download the model from HuggingFace," but the first time you visit, you're completely lost—tens of thousands of models, hundreds of thousands of datasets, plus Spaces, Pipelines... how do you even use it?

💡 What You Will Learn

Every tutorial tells you to "go download the model from HuggingFace," but the first time you visit, you're completely lost—tens of thousands of models, hundreds of thousands of datasets, plus Spaces,

HuggingFaceWhat Is

pip install huggingface_hub

from huggingface_hub import snapshot_download

# 
snapshot_download(
    repo_id="Qwen/Qwen2.5-7B-Instruct-GGUF",
    local_dir="./models/qwen2.5",
    ignore_patterns=["*.safetensors"]  # GGUF
)

# 
huggingface-cli download Qwen/Qwen2.5-7B-Instruct-GGUF qwen2.5-7b-instruct-q4_k_m.gguf --local-dir ./models

pip install transformers

from transformers import pipeline

# 
classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased")
result = classifier("I love HuggingFace!")
print(result)
# [{'label': 'POSITIVE', 'score': 0.9998}]

# 1. 
huggingface-cli login

# 2. New Model

# 3. 
huggingface-cli upload your-username/your-model ./local-model-folder

import gradio as gr

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

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