riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-07-17 19:48:23 +06:00
Code Issues Actions 33 Packages Projects Releases Wiki Activity

Reorder the code on the Hub to explicit that sharing on the Hub isn't a requirement (#27691)

Browse Source 
Reorder
This commit is contained in:
Lysandre Debut 2023-11-27 09:38:18 +01:00 committed by GitHub
parent 5c30dd40e7
commit 27b752bcf1
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 24 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

46
docs/source/en/custom_models.md
View File

@ -14,7 +14,7 @@ rendered properly in your Markdown viewer.
--> -->
# Sharing custom models # Building custom models
The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder
of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs. of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs.
@ -22,7 +22,8 @@ of the repository with no abstraction, so you can easily copy a modeling file an
If you are writing a brand new model, it might be easier to start from scratch. In this tutorial, we will show you If you are writing a brand new model, it might be easier to start from scratch. In this tutorial, we will show you
how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it how to write a custom model and its configuration so it can be used inside Transformers, and how you can share it
with the community (with the code it relies on) so that anyone can use it, even if it's not present in the 🤗 with the community (with the code it relies on) so that anyone can use it, even if it's not present in the 🤗
Transformers library. Transformers library. We'll see how to build upon transformers and extend the framework with your hooks and
custom code.
We will illustrate all of this on a ResNet model, by wrapping the ResNet class of the We will illustrate all of this on a ResNet model, by wrapping the ResNet class of the
[timm library](https://github.com/rwightman/pytorch-image-models) into a [`PreTrainedModel`]. [timm library](https://github.com/rwightman/pytorch-image-models) into a [`PreTrainedModel`].
@ -218,6 +219,27 @@ resnet50d.model.load_state_dict(pretrained_model.state_dict())
Now let's see how to make sure that when we do [`~PreTrainedModel.save_pretrained`] or [`~PreTrainedModel.push_to_hub`], the Now let's see how to make sure that when we do [`~PreTrainedModel.save_pretrained`] or [`~PreTrainedModel.push_to_hub`], the
code of the model is saved. code of the model is saved.
## Registering a model with custom code to the auto classes
If you are writing a library that extends 🤗 Transformers, you may want to extend the auto classes to include your own
model. This is different from pushing the code to the Hub in the sense that users will need to import your library to
get the custom models (contrarily to automatically downloading the model code from the Hub).
As long as your config has a `model_type` attribute that is different from existing model types, and that your model
classes have the right `config_class` attributes, you can just add them to the auto classes like this:
```py
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)
```
Note that the first argument used when registering your custom config to [`AutoConfig`] needs to match the `model_type`
of your custom config, and the first argument used when registering your custom models to any auto model class needs
to match the `config_class` of those models.
## Sending the code to the Hub ## Sending the code to the Hub
<Tip warning={true}> <Tip warning={true}>
@ -350,23 +372,3 @@ model = AutoModelForImageClassification.from_pretrained(
Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit Note that when browsing the commit history of the model repo on the Hub, there is a button to easily copy the commit
hash of any commit. hash of any commit.
## Registering a model with custom code to the auto classes
If you are writing a library that extends 🤗 Transformers, you may want to extend the auto classes to include your own
model. This is different from pushing the code to the Hub in the sense that users will need to import your library to
get the custom models (contrarily to automatically downloading the model code from the Hub).
As long as your config has a `model_type` attribute that is different from existing model types, and that your model
classes have the right `config_class` attributes, you can just add them to the auto classes like this:
```py
from transformers import AutoConfig, AutoModel, AutoModelForImageClassification
AutoConfig.register("resnet", ResnetConfig)
AutoModel.register(ResnetConfig, ResnetModel)
AutoModelForImageClassification.register(ResnetConfig, ResnetModelForImageClassification)
```
Note that the first argument used when registering your custom config to [`AutoConfig`] needs to match the `model_type`
of your custom config, and the first argument used when registering your custom models to any auto model class needs
to match the `config_class` of those models.