Model sharing and uploading

In this page, we will show you how to share a model you have trained or fine-tuned on new data with the community on the model hub.

We have seen in the training tutorial: how to fine-tune a model on a given task. You have probably done something similar on your task, either using the model directly in your own training loop or using the Trainer/TFTrainer class. Let’s see how you can share the result on the model hub.

Model versioning

Since version v3.5.0, the model hub has built-in model versioning based on git and git-lfs. It is based on the paradigm that one model is one repo.

This allows:

• built-in versioning
• access control
• scalability

This is built around revisions, which is a way to pin a specific version of a model, using a commit hash, tag or branch.

For instance:

>>> model = AutoModel.from_pretrained(
>>>   "julien-c/EsperBERTo-small",
>>>   revision="v2.0.1" # tag name, or branch name, or commit hash
>>> )

Push your model from Python

Preparation

The first step is to make sure your credentials to the hub are stored somewhere. This can be done in two ways. If you have access to a terminal, you cam just run the following command in the virtual environment where you installed 🤗 Transformers:

transformers-cli login

It will store your access token in the Hugging Face cache folder (by default ~/.cache/).

If you don’t have an easy access to a terminal (for instance in a Colab session), you can find a token linked to your account by going on huggingface.co <https://huggingface.co./>, click on your avatar on the top left corner, then on Edit profile on the left, just beneath your profile picture. In the submenu API Tokens, you will find your API token that you can just copy.

Directly push your model to the hub

Once you have an API token (either stored in the cache or copied and pasted in your notebook), you can directly push a finetuned model you saved in save_directory by calling:

finetuned_model.push_to_hub("my-awesome-model")

If you have your API token not stored in the cache, you will need to pass it with use_auth_token=your_token. This is also be the case for all the examples below, so we won’t mention it again.

This will create a repository in your namespace name my-awesome-model, so anyone can now run:

from transformers import AutoModel

model = AutoModel.from_pretrained("your_username/my-awesome-model")

Even better, you can combine this push to the hub with the call to save_pretrained:

finetuned_model.save_pretrained(save_directory, push_to_hub=True, repo_name="my-awesome-model")

If you are a premium user and want your model to be private, just add private=True to this call.

If you are a member of an organization and want to push it inside the namespace of the organization instead of yours, just add organization=my_amazing_org.

Add new files to your model repo

Once you have pushed your model to the hub, you might want to add the tokenizer, or a version of your model for another framework (TensorFlow, PyTorch, Flax). This is super easy to do! Let’s begin with the tokenizer. You can add it to the repo you created before like this

tokenizer.push_to_hub("my-awesome-model")

If you know its URL (it should be https://huggingface.co./username/repo_name), you can also do:

tokenizer.push_to_hub(repo_url=my_repo_url)

And that’s all there is to it! It’s also a very easy way to fix a mistake if one of the files online had a bug.

To add a model for another backend, it’s also super easy. Let’s say you have fine-tuned a TensorFlow model and want to add the pytorch model files to your model repo, so that anyone in the community can use it. The following allows you to directly create a PyTorch version of your TensorFlow model:

from transformers import AutoModel

model = AutoModel.from_pretrained(save_directory, from_tf=True)

You can also replace save_directory by the identifier of your model (username/repo_name) if you don’t have a local save of it anymore. Then, just do the same as before:

model.push_to_hub("my-awesome-model")

or

model.push_to_hub(repo_url=my_repo_url)

Use your terminal and git

Basic steps

In order to upload a model, you’ll need to first create a git repo. This repo will live on the model hub, allowing users to clone it and you (and your organization members) to push to it.

You can create a model repo directly from the /new page on the website.

Alternatively, you can use the transformers-cli. The next steps describe that process:

Go to a terminal and run the following command. It should be in the virtual environment where you installed 🤗 Transformers, since that command transformers-cli comes from the library.

transformers-cli login

Once you are logged in with your model hub credentials, you can start building your repositories. To create a repo:

transformers-cli repo create your-model-name

If you want to create a repo under a specific organization, you should add a —organization flag:

transformers-cli repo create your-model-name --organization your-org-name

This creates a repo on the model hub, which can be cloned.

# Make sure you have git-lfs installed
# (https://git-lfs.github.com/)
git lfs install

git clone https://huggingface.co./username/your-model-name

When you have your local clone of your repo and lfs installed, you can then add/remove from that clone as you would with any other git repo.

# Commit as usual
cd your-model-name
echo "hello" >> README.md
git add . && git commit -m "Update from $USER"

We are intentionally not wrapping git too much, so that you can go on with the workflow you’re used to and the tools you already know.

The only learning curve you might have compared to regular git is the one for git-lfs. The documentation at git-lfs.github.com is decent, but we’ll work on a tutorial with some tips and tricks in the coming weeks!

Additionally, if you want to change multiple repos at once, the change_config.py script can probably save you some time.

Make your model work on all frameworks

You probably have your favorite framework, but so will other users! That’s why it’s best to upload your model with both PyTorch and TensorFlow checkpoints to make it easier to use (if you skip this step, users will still be able to load your model in another framework, but it will be slower, as it will have to be converted on the fly). Don’t worry, it’s super easy to do (and in a future version, it might all be automatic). You will need to install both PyTorch and TensorFlow for this step, but you don’t need to worry about the GPU, so it should be very easy. Check the TensorFlow installation page and/or the PyTorch installation page to see how.

First check that your model class exists in the other framework, that is try to import the same model by either adding or removing TF. For instance, if you trained a DistilBertForSequenceClassification, try to type

>>> from transformers import TFDistilBertForSequenceClassification

and if you trained a TFDistilBertForSequenceClassification, try to type

>>> from transformers import DistilBertForSequenceClassification

This will give back an error if your model does not exist in the other framework (something that should be pretty rare since we’re aiming for full parity between the two frameworks). In this case, skip this and go to the next step.

Now, if you trained your model in PyTorch and have to create a TensorFlow version, adapt the following code to your model class:

>>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
>>> tf_model.save_pretrained("path/to/awesome-name-you-picked")

and if you trained your model in TensorFlow and have to create a PyTorch version, adapt the following code to your model class:

>>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
>>> pt_model.save_pretrained("path/to/awesome-name-you-picked")

That’s all there is to it!

Check the directory before pushing to the model hub.

Make sure there are no garbage files in the directory you’ll upload. It should only have:

• a config.json file, which saves the configuration of your model ;
• a pytorch_model.bin file, which is the PyTorch checkpoint (unless you can’t have it for some reason) ;
• a tf_model.h5 file, which is the TensorFlow checkpoint (unless you can’t have it for some reason) ;
• a special_tokens_map.json, which is part of your tokenizer save;
• a tokenizer_config.json, which is part of your tokenizer save;
• files named vocab.json, vocab.txt, merges.txt, or similar, which contain the vocabulary of your tokenizer, part of your tokenizer save;
• maybe a added_tokens.json, which is part of your tokenizer save.

Other files can safely be deleted.

Uploading your files

Once the repo is cloned, you can add the model, configuration and tokenizer files. For instance, saving the model and tokenizer files:

>>> model.save_pretrained("path/to/repo/clone/your-model-name")
>>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")

Or, if you’re using the Trainer API

>>> trainer.save_model("path/to/awesome-name-you-picked")
>>> tokenizer.save_pretrained("path/to/repo/clone/your-model-name")

You can then add these files to the staging environment and verify that they have been correctly staged with the git status command:

git add --all
git status

Finally, the files should be committed:

git commit -m "First version of the your-model-name model and tokenizer."

And pushed to the remote:

git push

This will upload the folder containing the weights, tokenizer and configuration we have just prepared.

Add a model card

To make sure everyone knows what your model can do, what its limitations, potential bias or ethical considerations are, please add a README.md model card to your model repo. You can just create it, or there’s also a convenient button titled “Add a README.md” on your model page. A model card documentation can be found here (meta-suggestions are welcome). model card template (meta-suggestions are welcome).

If your model is fine-tuned from another model coming from the model hub (all 🤗 Transformers pretrained models do), don’t forget to link to its model card so that people can fully trace how your model was built.

Using your model

Your model now has a page on huggingface.co/models 🔥

Anyone can load it from code:

>>> tokenizer = AutoTokenizer.from_pretrained("namespace/awesome-name-you-picked")
>>> model = AutoModel.from_pretrained("namespace/awesome-name-you-picked")

You may specify a revision by using the revision flag in the from_pretrained method:

>>> tokenizer = AutoTokenizer.from_pretrained(
>>>   "julien-c/EsperBERTo-small",
>>>   revision="v2.0.1" # tag name, or branch name, or commit hash
>>> )

Workflow in a Colab notebook

If you’re in a Colab notebook (or similar) with no direct access to a terminal, here is the workflow you can use to upload your model. You can execute each one of them in a cell by adding a ! at the beginning.

First you need to install git-lfs in the environment used by the notebook:

sudo apt-get install git-lfs

Then you can use either create a repo directly from huggingface.co , or use the transformers-cli to create it:

transformers-cli login
transformers-cli repo create your-model-name

Once it’s created, you can clone it and configure it (replace username by your username on huggingface.co):

git lfs install

git clone https://username:[email protected]/username/your-model-name
# Alternatively if you have a token,
# you can use it instead of your password
git clone https://username:[email protected]/username/your-model-name

cd your-model-name
git config --global user.email "[email protected]"
# Tip: using the same email than for your huggingface.co account will link your commits to your profile
git config --global user.name "Your name"

Once you’ve saved your model inside, and your clone is setup with the right remote URL, you can add it and push it with usual git commands.

git add .
git commit -m "Initial commit"
git push