Overview
What is Model Hub?
Model Hub is basically a central repository of AI models.
You can browse, download, share, and version-control models.
Provide tools for training, fine-tuning, and deploying models.
How does it work?
Step 1. Pick a Base Model from the Model Catalog
Search the model (e.g.,
"Llama-3.2-1B"for NLP,"Qwen2-VL-72B"for vision).These models already learned general representations.
Step 2. Launch Fine-tuning
Use selected model to start fine-tuning
Step 3. Push Fine-tuned Model Back to the Hub
After training, you upload your fine-tuned model to the hub.
It gets a new version ID and can be shared with collaborators (or kept private).
Step 4. Use Anywhere
Once fine-tuned, you can download the model by SDK.
This ensures reproducibility and easy deployment.
Why Model Hub?
Centralized Access to Models
Without a hub, everyone would have to hunt for models on GitHub repos, random blogs, or papers.
Model Hub is a single catalog where you can:
Search models by task (
text-classification,speech-to-text,image-segmentation).Compare architectures and benchmarks.
Reuse models with just one line of code.
Reusability & Efficiency
Training large models from scratch is expensive.
Hubs let you reuse pretrained checkpoints, so you only need to fine-tune.
Collaboration & Sharing
Teams can push fine-tuned models to a hub → other team members can pull them instantly.
This works for code: version control, forks, and community contributions.
Deployment Ready
That means once your model is in the hub, you can:
Deploy it on cloud infrastructure.
Use it via REST APIs.
Scale it without managing servers.
Governance & Version Control
Hubs track different versions of models.
You know exactly which checkpoint was used in production (important for MLOps & audits).
You can mark models as public, private, or restricted.
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