How to Train and Publish Your Own LLM with Hugging Face (Part 3: Publishing & Sharing)

Welcome to the final part of our series on training and publishing your own Large Language Model with Hugging Face! 🎉

👉 If you’re landing here directly, I recommend first checking out the earlier posts:

• Part 1: Getting Started – Installing Hugging Face, running a model, and creating a dataset.
• Part 2: Fine-Tuning Your Model – Training your own custom model step by step.

In this post, we’ll cover the most exciting part: publishing your model to Hugging Face Hub and sharing it with others.

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What You’ll Learn in This Post

• How to log in to Hugging Face Hub
• How to push your trained model to your profile
• How to add model cards (documentation)
• How to create a simple demo using Hugging Face Spaces
• How to share your model with others

By the end, your model will be online, accessible to others, and even usable in apps! 🌍

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Step 1: Log In to Hugging Face Hub

First, install the CLI if you haven’t already:

pip install huggingface_hub

Then log in:

huggingface-cli login

👉 This will ask for your Hugging Face access token. You can get it from your Hugging Face settings.

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Step 2: Push Your Model to the Hub

From Part 2, you already have your model saved locally (my_custom_model). Let’s upload it:

from huggingface_hub import HfApi, HfFolder, Repository
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "my-username/my-custom-model"

model.push_to_hub(model_name)
tokenizer.push_to_hub(model_name)

Now your model is live on your Hugging Face profile! 🎉

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Step 3: Add a Model Card

When someone visits your model page, they’ll see a Model Card. This is like documentation for your model. You can edit it directly in the Hugging Face web UI.

Things to include:

• 📌 What your model does
• 📊 What dataset you trained on
• ⚠️ Limitations or biases
• 💡 Example usage code

A simple starter model card:

# My Custom Model
This is a fine-tuned GPT-2 model trained on my own dataset.

## How to Use
```python
from transformers import pipeline

generator = pipeline("text-generation", model="my-username/my-custom-model")
print(generator("Hello world", max_length=50))

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Step 4: Create a Demo with Hugging Face Spaces

Want others to try your model in their browser? Hugging Face Spaces lets you build small web apps.

• Go to Hugging Face Spaces
• Click New Space
• Choose Gradio or Streamlit as your app framework

Example app.py using Gradio:

import gradio as gr
from transformers import pipeline

generator = pipeline("text-generation", model="my-username/my-custom-model")

def generate_text(prompt):
    return generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']

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

Now your model has an interactive demo anyone can use! ✨

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Step 5: Share Your Model

Congratulations — your model is now live! You can share the Hugging Face link with:

• Your teammates 👩‍💻
• Your research community 🧑‍🔬
• Or embed it into your own apps 🚀

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Wrap-Up

In this post, you:

• Logged in to Hugging Face Hub
• Published your model online
• Wrote a model card
• Created an interactive demo with Spaces

🎯 That’s it! You now know how to train, fine-tune, and publish your own LLM using Hugging Face.

This 3-part series showed you the full journey from zero to sharing your AI model with the world. 🌍