# 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 | ReFT | | --------------------------------------- | -| - | - | - | - | - | - |- | - | | [Custom models](plugin_interface.html) | ✅ | | ✅ | ✅ | ✅ | ✅ | | ✅ | ✅ | | [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) | (*) | (*) | (*) | (*) | (*) | (*) | | | (*) | | Gemma 2 | ✅ | | ✅ | ✅ | ✅ | ✅ | | | ✅ | | Gemma 3 (Text) | ✅ | | ✅ | ✅ | ✅ | ✅ | | | ✅ | | [GPT-2](classes/models/gpt2.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | [GPT-J](classes/models/gptj.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | [Llama
Llama 2
Llama 3](classes/models/llama.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | [MBart](classes/models/mbart.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | ModernBERT | ✅ | | ✅ | ✅ | ✅ | ✅ | | | ✅ | | [Mistral](classes/models/mistral.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | [MT5](classes/models/mt5.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | Phi-1
Phi-2 | ✅ | | ✅ | ✅ | ✅ | ✅ | | | ✅ | | [PLBart](classes/models/plbart.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | Qwen2
Qwen2.5
Qwen3 | ✅ | | ✅ | ✅ | ✅ | ✅ | | | ✅ | | [RoBERTa](classes/models/roberta.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [T5](classes/models/t5.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | [ViT](classes/models/vit.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [Whisper](classes/models/whisper.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | ✅ | | [XLM-RoBERTa](classes/models/xlmroberta.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | | [X-MOD](classes/models/xmod.html) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | Models supported via the [plugin interface mechanism](plugin_interface.html). `original_ln_after=False` is unsupported for bottleneck configs. (*) If the used encoder and decoder model class are supported. **Missing a model architecture you'd like to use?** The new model plugin interface makes it easy to support new transformer models with just a few lines of code [Learn more](plugin_interface.md). Also, _Adapters_ can be 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!_