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Create a new model

From the website

Hub documentation

Take a first look at the Hub features

Programmatic access

Use the Hub’s Python client library

Getting started with our git and git-lfs interface

You can create a repository from the CLI (skip if you created a repo from the website)

						$pip install huggingface_hub
						#You already have it if you installed transformers or datasets
	
						$huggingface-cli login
						#Log in using a token from huggingface.co/settings/tokens
						#Create a model or dataset repo from the CLI if needed
						$huggingface-cli repo create repo_name --type {model, dataset, space}

Clone your model, dataset or Space locally

					#Make sure you have git-lfs installed
					#(https://git-lfs.github.com)
					$git lfs install
					$git clone https://huggingface.co/username/repo_name

Then add, commit and push any file you want, including larges files

						# save files via `.save_pretrained()` or move them here
						$git add .
						$git commit -m "commit from $USER"
						$git push

In most cases, if you're using one of the compatible libraries, your repo will then be accessible from code, through its identifier: username/repo_name

For example for a transformers model, anyone can load it with:

					tokenizer = AutoTokenizer.from_pretrained("username/repo_name")
					model = AutoModel.from_pretrained("username/repo_name")