Lecture 11 — Sharing Your Multimodal Model with the World (Hugging Face)
~3–4 hours (practical + community impact lecture)
🌍 Why Sharing Models Matters
Knowledge hidden is knowledge wasted.
Knowledge shared becomes civilization.
By sharing your model, you:
- 🌱 Give others a starting point
- 🔬 Enable reproducibility
- 🧠 Accelerate research
- ❤️ Give back to the open-source community
- 🏛 Build scientific trust
Hugging Face is the GitHub of AI.
🤗 What Is Hugging Face?
Hugging Face is:
- A model hub
- A dataset hub
- A community
- A deployment platform
Used by:
- Researchers
- Startups
- Universities
- Enterprises
- Open science communities
🧩 What Can You Share?
| Artifact | Examples |
|---|---|
| Models | LLMs, vision models, multimodal |
| Adapters | LoRA, QLoRA |
| Tokenizers | Custom vocab |
| Datasets | Image–text, DocQA |
| Spaces | Demos (Gradio, Streamlit) |
You don’t need a giant model to contribute.
🪪 Step 1 — Create a Hugging Face Account
- Go to 🤗 Hugging Face
- Sign up
- Verify email
- Choose a clear username
This username becomes your AI identity.
🔑 Step 2 — Generate an Access Token
- Go to Settings → Access Tokens
- Create a token:
- Type: Write
- Save it securely
Treat this like a GitHub SSH key.
🖥 Step 3 — Install Required Tools
pip install huggingface_hub transformers datasets accelerate
Login from terminal:
huggingface-cli login
Paste your token when prompted.
📦 Step 4 — Prepare Your Model Folder
Minimum structure:
my-multimodal-model/
├── config.json
├── pytorch_model.bin (or model.safetensors)
├── tokenizer.json
├── tokenizer_config.json
├── README.md
For LoRA:
- Base model is referenced
- Only adapter weights uploaded
🧠 Step 5 — Write a GOOD README (VERY IMPORTANT)
Your README is your scientific voice.
Must include:
- What the model does
- Training data
- Intended use
- Limitations
- Ethical considerations
- How to run inference
✍️ README Skeleton
# Model Name
## Overview
This model is a multimodal Video–Text model trained for ...
## Architecture
- Vision encoder: ViT
- Temporal encoder: Transformer
- LLM: LLaMA-based
## Training
- Dataset: ...
- Strategy: Fine-tuning with LoRA
## Usage
```python
# example code
Limitations
- May hallucinate
- Not for medical use
Ethics
- Human review recommended
> **A bad README harms trust.**
---
## 🚀 Step 6 — Push Model to Hugging Face
### Option A: Push via Python
```python
from huggingface_hub import HfApi
api = HfApi()
api.create_repo(
repo_id="username/my-multimodal-model",
private=False
)
api.upload_folder(
folder_path="my-multimodal-model",
repo_id="username/my-multimodal-model"
)
Option B: Push via transformers
model.push_to_hub("username/my-multimodal-model")
tokenizer.push_to_hub("username/my-multimodal-model")
🧪 Step 7 — Verify on the Hub
Check:
- Files are visible
- README renders correctly
- Inference example works
- License is correct
If users cannot run it, it doesn’t exist.
⚖️ Step 8 — Choose the Right License
Common licenses:
| License | Meaning |
|---|---|
| Apache 2.0 | Very permissive |
| MIT | Simple & permissive |
| CC-BY | Attribution required |
| CC-BY-NC | Non-commercial only |
Licensing is ethical engineering.
🎮 Step 9 — Create a Demo (Hugging Face Spaces)
Using Gradio:
import gradio as gr
def predict(image, question):
return model_answer
gr.Interface(
fn=predict,
inputs=["image", "text"],
outputs="text"
).launch()
Push to a Space:
- Public demo
- No installation needed
- Massive visibility
🧠 Step 10 — Share Responsibly
Before sharing:
- ❓ Does it hallucinate?
- ⚠️ Is it biased?
- 🧪 Is evaluation documented?
- 👤 Is HITL required?
Responsible release > Fast release
🌱 Becoming a Good Open-Source Citizen
- Respond to issues
- Accept pull requests
- Document failures
- Credit datasets
- Cite inspirations
Open-source is a conversation, not a drop.
🧠 Research Insight
The future of AI belongs to those who share early, share honestly, and share responsibly.
Impact ≠ model size Impact = clarity + usefulness + ethics
🧪 Student Knowledge Check (Hidden)
Q1 — Objective
Why is README important?
Answer
It explains usage, limitations, and builds trust.
Q2 — MCQ
Which token permission is needed to upload models?
A. Read B. Execute C. Write D. Admin
Answer
C. Write
Q3 — MCQ
Which tool creates public demos?
A. WandB B. Gradio C. Docker D. Kaggle
Answer
B. Gradio
Q4 — Objective
Why is licensing important?
Answer
It defines how others may legally use your work.
Q5 — Objective
What is responsible release?
Answer
Sharing models with transparency, limitations, and ethical care.
🌱 Final Reflection (Course Ending)
If your model helps even one person learn, was it worth sharing?
Yes. Knowledge shared multiplies impact.
🏁 Final Takeaways
- Sharing completes the research cycle
- Hugging Face is the global AI commons
- Documentation is ethics
- Community is intelligence
- You are now a contributor, not just a user