# Model Overview This page gives an overview of the Transformer models currently supported by `adapters`. The table below further shows which model architectures support which adaptation methods and which features of `adapters`. ```{eval-rst} .. note:: Each supported model architecture X typically provides a class ``XAdapterModel`` for usage with ``AutoAdapterModel``. Additionally, it is possible to use adapters with the model classes already shipped with Hugging Face Transformers. For these classes, initialize the model for adapters with `adapters.init(model)`. E.g., for BERT, this means adapters provides a ``BertAdapterModel`` class, but you can also use ``BertModel``, ``BertForSequenceClassification`` etc. together with adapters. ``` | Model | (Bottleneck)
Adapters | Prefix
Tuning | LoRA | Compacter | Adapter
Fusion | Invertible
Adapters | Parallel
block | Prompt
Tuning | | --------------------------------------- | -| - | - | - | - | - | - |- | | [ALBERT](classes/models/albert.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [BART](classes/models/bart.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [BEIT](classes/models/beit.html) | ✅ | ✅ | ✅ | ✅ | ✅ | | | ✅ | | [BERT-Generation](classes/models/bert-generation.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [BERT](classes/models/bert.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [CLIP](classes/models/clip.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | | [DeBERTa](classes/models/deberta.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [DeBERTa-v2](classes/models/debertaV2.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [DistilBERT](classes/models/distilbert.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [Electra](classes/models/electra.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [Encoder Decoder](classes/models/encoderdecoder.html) | (*) | (*) | (*) | (*) | (*) | (*) | | | | [GPT-2](classes/models/gpt2.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [GPT-J](classes/models/gptj.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [Llama](classes/models/llama.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [MBart](classes/models/mbart.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [MT5](classes/models/mt5.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [RoBERTa](classes/models/roberta.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [T5](classes/models/t5.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | | [ViT](classes/models/vit.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [XLM-RoBERTa](classes/models/xlmroberta.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [X-MOD](classes/models/xmod.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | (*) If the used encoder and decoder model class are supported. **Missing a model architecture you'd like to use?** adapters can be easily extended to new model architectures as described in [Adding Adapters to a Model](https://docs.adapterhub.ml/contributing/adding_adapters_to_a_model.html). Feel free to [open an issue](https://github.com/Adapter-Hub/adapters/issues) requesting support for a new architecture. _We very much welcome pull requests adding new model implementations!_