@@ -22,16 +22,14 @@ the `model hub <https://huggingface.co/models>`__.
2222
2323 Optionally, you can join an existing organization or create a new one.
2424
25- Prepare your model for uploading
26- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
2725
2826We have seen in the :doc: `training tutorial <training >`: how to fine-tune a model on a given task. You have probably
2927done something similar on your task, either using the model directly in your own training loop or using the
3028:class: `~.transformers.Trainer `/:class: `~.transformers.TFTrainer ` class. Let's see how you can share the result on the
3129`model hub <https://huggingface.co/models >`__.
3230
3331Model versioning
34- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
32+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
3533
3634Since version v3.5.0, the model hub has built-in model versioning based on git and git-lfs. It is based on the paradigm
3735that one model *is * one repo.
@@ -54,6 +52,106 @@ For instance:
5452 >>> revision="v2.0.1" # tag name, or branch name, or commit hash
5553 >>> )
5654
55+
56+
57+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
58+
59+ Preparation
60+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
61+
62+ The first step is to make sure your credentials to the hub are stored somewhere. This can be done in two ways. If you
63+ have access to a terminal, you cam just run the following command in the virtual environment where you installed 🤗
64+ Transformers:
65+
66+ .. code-block :: bash
67+
68+ transformers-cli login
69+
70+ :obj: `~/.cache/ `).
71+
72+ 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
73+ acount by going on `huggingface.co <https://huggingface.co/> `, click on your avatar on the top left corner, then on
74+ `Edit profile ` on the left, just beneath your profile picture. In the submenu `API Tokens `, you will find your API
75+ token that you can just copy.
76+
77+ Directly push your model to the hub
78+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
79+
80+ Once you have an API token (either stored in the cache or copied and pasted in your notebook), you can directly push a
81+ finetuned model you saved in :obj: `save_drectory ` by calling:
82+
83+ .. code-block :: python
84+
85+ finetuned_model.push_to_hub(" my-awesome-model"
86+
87+ :obj: `use_auth_token=your_token `.
88+ This is also be the case for all the examples below, so we won't mention it again.
89+
90+ This will create a repository in your namespace name :obj: `my-awesome-model `, so anyone can now run:
91+
92+ .. code-block :: python
93+
94+ from transformers import AutoModel
95+
96+ model = AutoModel.from_pretrained(" your_username/my-awesome-model"
97+
98+ :obj: `save_pretrained `:
99+
100+ .. code-block :: python
101+
102+ finetuned_model.save_pretrained(save_directory, push_to_hub = True , repo_name = " my-awesome-model"
103+
104+ :obj: `private=True ` to this call.
105+
106+ If you are a member of an organization and want to push it inside the namespace of the organization instead of yours,
107+ just add :obj: `organization=my_amazing_org `.
108+
109+ Add new files to your model repo
110+ ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
111+
112+ Once you have pushed your model to the hub, you might want to add the tokenizer, or a version of your model for another
113+ framework (TensorFlow, PyTorch, Flax). This is super easy to do! Let's begin with the tokenizer. You can add it to the
114+ repo you created before like this
115+
116+ .. code-block :: python
117+
118+ tokenizer.push_to_hub(" my-awesome-model"
119+
120+ :obj: `https://huggingface.co/username/repo_name `), you can also do:
121+
122+ .. code-block :: python
123+
124+ tokenizer.push_to_hub(repo_url = my_repo_url)
125+
126+
127+
128+ 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
129+ add the pytorch model files to your model repo, so that anyone in the community can use it. The following allows you to
130+ directly create a PyTorch version of your TensorFlow model:
131+
132+ .. code-block :: python
133+
134+ from transfomers import AutoModel
135+
136+ model = AutoModel.from_pretrained(save_directory, from_tf = True )
137+
138+ :obj: `save_directory ` by the identifier of your model (:obj: `username/repo_name `) if you don't
139+ have a local save of it anymore. Then, just do the same as before:
140+
141+ .. code-block :: python
142+
143+ model.push_to_hub(" my-awesome-model"
144+
145+
146+
147+ .. code-block :: python
148+
149+ model.push_to_hub(repo_url = my_repo_url)
150+
151+
152+
153+ ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
154+
57155Basic steps
58156^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
59157
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