# Models

With the `AutoModelForCausalLMWithValueHead` class TRL supports all decoder model architectures in transformers such as GPT-2, OPT, and GPT-Neo. In addition, with `AutoModelForSeq2SeqLMWithValueHead` you can use encoder-decoder architectures such as T5. TRL also requires reference models which are frozen copies of the model that is trained. With `create_reference_model` you can easily create a frozen copy and also share layers between the two models to save memory.

## PreTrainedModelWrapper

[[autodoc]] PreTrainedModelWrapper

## AutoModelForCausalLMWithValueHead

[[autodoc]] AutoModelForCausalLMWithValueHead
    - __init__
    - forward
    - generate
    - _init_weights

## AutoModelForSeq2SeqLMWithValueHead

[[autodoc]] AutoModelForSeq2SeqLMWithValueHead
    - __init__
    - forward
    - generate
    - _init_weights

## create_reference_model

[[autodoc]] create_reference_model
