Overview: Efficient Fine-Tuning Methods

Large pre-trained Transformer-based language models (LMs) have become the foundation of NLP in recent years. While the most prevalent method of using these LMs for transfer learning involves costly full fine-tuning of all model parameters, a series of efficient and lightweight alternatives have been established in recent time. Instead of updating all parameters of the pre-trained LM towards a downstream target task, these methods commonly introduce a small amount of new parameters and only update these while keeping the pre-trained model weights fixed.

Why use Efficient Fine-Tuning?

Efficient fine-tuning methods offer multiple benefits over full fine-tuning of LMs:

  • They are parameter-efficient, i.e. they only update a very small subset (often under 1%) of a model’s parameters.

  • They often are modular, i.e. the updated parameters can be extracted and shared independently of the base model parameters.

  • They are easy to share and easy to deploy due to their small file sizes, e.g. having only ~3MB per task instead of ~440MB for sharing a full model.

  • They speed up training, i.e. efficient fine-tuning often needs less time for training compared fully fine-tuning LMs.

  • They are composable, e.g. multiple adapters trained on different tasks can be stacked, fused or mixed to leverage their combined knowledge.

  • They often provide on-par performance with full fine-tuning.

More specifically, let the parameters of a LM be composed of a set of pre-trained parameters \(\Theta\) (frozen) and a set of (newly introduced) parameters \(\Phi\). Then, efficient fine-tuning methods optimize only \(\Phi\) according to a loss function \(L\) on a dataset \(D\):

\[ \Phi^* \leftarrow \arg \min_{\Phi} L(D; \{\Theta, \Phi\}) \]

Efficient fine-tuning might insert parameters \(\Phi\) at different locations of a Transformer-based LM. One early and successful method, (bottleneck) adapters, introduces bottleneck feed-forward layers in each layer of a Transformer model. While these adapters have laid the foundation of the adapter-transformers library, multiple alternative methods have been introduced and integrated since. In the following, we present all methods currently integrated into this library (see here for a tabular overview).

Configuration: All presented methods can be added, trained, saved and shared using the same set of model class functions (see class documentation). Each method is specified and configured using a specific configuration class, all of which derive from the common AdapterConfigBase class. E.g., adding one of the methods presented below to an existing model instance follows this scheme:

config = ... # config class deriving from AdapterConfigBase
model.add_adapter("name", config=config)


In literature, different terms are used to refer to efficient fine-tuning methods. The term “adapter” is usually only applied to bottleneck adapter modules. However, most efficient fine-tuning methods follow the same general idea of inserting a small set of new parameters and by this “adapting” the pre-trained LM to a new task. In adapter-transformers, the term “adapter” thus may refer to any efficient fine-tuning method if not specified otherwise.

Bottleneck Adapters

Configuration class: AdapterConfig

Bottleneck adapters introduce bottleneck feed-forward layers in each layer of a Transformer model. Generally, these adapter layers consist of a down-projection matrix \(W_{down}\) that projects the layer hidden states into a lower dimension \(d_{bottleneck}\), a non-linearity \(f\), an up-projection \(W_{up}\) that projects back into the original hidden layer dimension and a residual connection \(r\):

\[ h \leftarrow W_{up} \cdot f(W_{down} \cdot h) + r \]

Depending on the concrete adapter configuration, these layers can be introduced at different locations within a Transformer block. Further, residual connections, layer norms, activation functions and bottleneck sizes etc. can be configured.

The most important configuration hyperparameter to be highlighted here is the bottleneck dimension \(d_{bottleneck}\). In adapter-transformers, this bottleneck dimension is specified indirectly via the reduction_factor attribute of a configuration. This reduction_factor defines the ratio between a model’s layer hidden dimension and the bottleneck dimension, i.e.:

\[ \text{reduction_factor} = \frac{d_{hidden}}{d_{bottleneck}} \]

A visualization of further configuration options related to the adapter structure is given in the figure below. For more details, refer to the documentation of AdapterConfig.

Adapter architectures

Visualization of possible adapter configurations with corresponding dictionary keys.

adapter-transformers comes with pre-defined configurations for some bottleneck adapter architectures proposed in literature:


from transformers.adapters import AdapterConfig

config = AdapterConfig(mh_adapter=True, output_adapter=True, reduction_factor=16, non_linearity="relu")
model.add_adapter("bottleneck_adapter", config=config)


Language Adapters - Invertible Adapters

Configuration class: PfeifferInvConfig, HoulsbyInvConfig

The MAD-X setup (Pfeiffer et al., 2020) proposes language adapters to learn language-specific transformations. After being trained on a language modeling task, a language adapter can be stacked before a task adapter for training on a downstream task. To perform zero-shot cross-lingual transfer, one language adapter can simply be replaced by another.

In terms of architecture, language adapters are largely similar to regular bottleneck adapters, except for an additional invertible adapter layer after the LM embedding layer. Embedding outputs are passed through this invertible adapter in the forward direction before entering the first Transformer layer and in the inverse direction after leaving the last Transformer layer. Invertible adapter architectures are further detailed in Pfeiffer et al. (2020) and can be configured via the inv_adapter attribute of the AdapterConfig class.


from transformers.adapters import PfeifferInvConfig

config = PfeifferInvConfig()
model.add_adapter("lang_adapter", config=config)



V1.x of adapter-transformers made a distinction between task adapters (without invertible adapters) and language adapters (with invertible adapters) with the help of the AdapterType enumeration. This distinction was dropped with v2.x.

Prefix Tuning

Configuration class: PrefixTuningConfig

Illustration of Prefix Tuning.

Illustration of the Prefix Tuning method within one Transformer layer. Trained components are colored in shades of magenta.

Prefix Tuning (Li and Liang, 2021) introduces new parameters in the multi-head attention blocks in each Transformer layer. More, specifically, it prepends trainable prefix vectors \(P^K\) and \(P^V\) to the keys and values of the attention head input, each of a configurable prefix length \(l\) (prefix_length attribute):

\[ head_i = \text{Attention}(Q W_i^Q, [P_i^K, K W_i^K], [P_i^V, V W_i^V]) \]

Following the original authors, the prefix vectors in \(P^K\) and \(P^V\) are note optimized directly, but reparameterized via a bottleneck MLP. This behavior is controlled via the flat attribute of the configuration. Using PrefixTuningConfig(flat=True) will create prefix tuning vectors that are optimized without reparameterization.


from transformers.adapters import PrefixTuningConfig

config = PrefixTuningConfig(flat=False, prefix_length=30)
model.add_adapter("prefix_tuning", config=config)

As reparameterization using the bottleneck MLP is not necessary for performing inference on an already trained Prefix Tuning module, adapter-transformers includes a function to “eject” a reparameterized Prefix Tuning into a flat one:


This will only retain the necessary parameters and reduces the size of the trained Prefix Tuning.



Configuration class: CompacterConfig, CompacterPlusPlusConfig

Illustration of Compacter.

Illustration of the Compacter method within one Transformer layer. Trained components are colored in shades of magenta.

The Compacter architecture proposed by Mahabadi et al., 2021 is similar to the bottleneck adapter architecture. It only exchanges the linear down- and up-projection with a PHM layer. Unlike the linear layer, the PHM layer constructs its weight matrix from two smaller matrices, which reduces the number of parameters. These matrices can be factorized and shared between all adapter layers. You can exchange the down- and up-projection layers from any of the bottleneck adapters described in the previous section for a PHM layer by specifying use_phm=True in the config.

The PHM layer has the following additional properties: phm_dim, shared_phm_rule, factorized_phm_rule, learn_phm, factorized_phm_W, shared_W_phm, phm_c_init, phm_init_range, hypercomplex_nonlinearity

For more information check out the AdapterConfig class.

To add a Compacter to your model you can use the predefined configs:

from transformers.adapters import CompacterConfig

config = CompacterConfig()
model.add_adapter("dummy", config=config)



Configuration class: LoRAConfig

Illustration of LoRA.

Illustration of the LoRA method within one Transformer layer. Trained components are colored in shades of magenta.

Low-Rank Adaptation (LoRA) is an efficient fine-tuning technique proposed by Hu et al. (2021). LoRA injects trainable low-rank decomposition matrices into the layers of a pre-trained model. For any model layer expressed as a matrix multiplication of the form \(h = W_0 x\), it therefore performs a reparameterization, such that:

\[ h = W_0 x + \frac{\alpha}{r} B A x \]

Here, \(A \in \mathbb{R}^{r\times k}\) and \(B \in \mathbb{R}^{d\times r}\) are the decomposition matrices and \(r\), the low-dimensional rank of the decomposition, is the most important hyperparameter.

While, in principle, this reparameterization can be applied to any weights matrix in a model, the original paper only adapts the attention weights of the Transformer self-attention sub-layer with LoRA. adapter-transformers additionally allows injecting LoRA into the dense feed-forward layers in the intermediate and output components of a Transformer block. You can configure the locations where LoRA weights should be injected using the attributes in the LoRAConfig class.


from transformers.adapters import LoRAConfig

config = LoRAConfig(r=8, alpha=16)
model.add_adapter("lora_adapter", config=config)

In the design of LoRA, Hu et al. (2021) also pay special attention to keeping the inference latency overhead compared to full fine-tuning at a minimum. To accomplish this, the LoRA reparameterization can be merged with the original pre-trained weights of a model for inference. Thus, the adapted weights are directly used in every forward pass without passing activations through an additional module. In adapter-transformers, this can be realized using the built-in merge_adapter() method:


To continue training on this LoRA adapter or to deactivate it entirely, the merged weights first have to be reset again:




Configuration class: IA3Config

Illustration of (IA)^3.

Illustration of the (IA)^3 method within one Transformer layer. Trained components are colored in shades of magenta.

Infused Adapter by Inhibiting and Amplifying Inner Activations ((IA)^3) is an efficient fine-tuning method proposed within the T-Few fine-tuning approach by Liu et al. (2022). (IA)^3 introduces trainable vectors \(l_W\) into different components of a Transformer model which perform element-wise rescaling of inner model activations. For any model layer expressed as a matrix multiplication of the form \(h = W x\), it therefore performs an element-wise multiplication with \(l_W\), such that:

\[ h = l_W \odot W x \]

Here, \(\odot\) denotes element-wise multiplication where the entries of \(l_W\) are broadcasted to the shape of \(W\).


from transformers.adapters import IA3Config

config = IA3Config()
model.add_adapter("ia3_adapter", config=config)

The implementation of (IA)^3, as well as the IA3Config class, are derived from the implementation of LoRA, with a few main modifications. First, (IA)^3 uses multiplicative composition of weights instead of additive composition as in LoRA. Second, the added weights are not further decomposed into low-rank matrices. Both of these modifications are controlled via the composition_mode configuration attribute by setting composition_mode="scale". Additionally, as the added weights are already of rank 1, r=1 is set.

Beyond that, both methods share the same configuration attributes that allow you to specify in which Transformer components rescaling vectors will be injected. Following the original implementation, IA3Config adds rescaling vectors to the self-attention weights (selfattn_lora=True) and the final feed-forward layer (output_lora=True). Further, you can modify which matrices of the attention mechanism to rescale by leveraging the attn_matrices attribute. By default, (IA)^3 injects weights into the key (‘k’) and value (‘v’) matrices, but not in the query (‘q’) matrix.

Finally, similar to LoRA, (IA)^3 also allows merging the injected parameters with the original weight matrices of the Transformer model. E.g.:

# Merge (IA)^3 adapter

# Reset merged weights


Method Combinations

Configuration class: ConfigUnion

While different efficient fine-tuning methods and configurations have often been proposed as standalone, it might be beneficial to combine them for joint training. To make this process easier, adapter-transformers provides the possibility to group multiple configuration instances together using the ConfigUnion class.

For example, this could be used to define different reduction factors for the adapter modules placed after the multi-head attention and the feed-forward blocks:

from transformers.adapters import AdapterConfig, ConfigUnion

config = ConfigUnion(
    AdapterConfig(mh_adapter=True, output_adapter=False, reduction_factor=16, non_linearity="relu"),
    AdapterConfig(mh_adapter=False, output_adapter=True, reduction_factor=2, non_linearity="relu"),
model.add_adapter("union_adapter", config=config)

Mix-and-Match Adapters

Configuration class: MAMConfig

He et al. (2021) study various variants and combinations of efficient fine-tuning methods. Among others, they propose Mix-and-Match Adapters as a combination of Prefix Tuning and parallel bottleneck adapters. This configuration is supported by adapter-transformers out-of-the-box:

from transformers.adapters import MAMConfig

config = MAMConfig()
model.add_adapter("mam_adapter", config=config)

and is identical to using the following ConfigUnion:

from transformers.adapters import ConfigUnion, ParallelConfig, PrefixTuningConfig

config = ConfigUnion(
model.add_adapter("mam_adapter", config=config)



Configuration class: UniPELTConfig

Illustration of UniPELT.

Illustration of the UniPELT method within one Transformer layer. Trained components are colored in shades of magenta.

An approach similar to the work of He et al. (2021) is taken by Mao et al. (2022) in their UniPELT framework. They, too, combine multiple efficient fine-tuning methods, namely LoRA, Prefix Tuning and bottleneck adapters, in a single unified setup. UniPELT additionally introduces a gating mechanism that controls the activation of the different submodules.

Concretely, for each adapted module \(m\), UniPELT adds a trainable gating value \(\mathcal{G}_m \in (0, 1)\) that is computed via a feed-forward network (\(W_{\mathcal{G}_m}\)) and sigmoid activation (\(\sigma\)) from the Transformer layer input states (\(x\)):

\[\mathcal{G}_m \leftarrow \sigma(W_{\mathcal{G}_m} \cdot x)\]

These gating values are then used to scale the output activations of the injected adapter modules, e.g. for a LoRA layer:

\[ h \leftarrow W_0 x + \mathcal{G}_{LoRA} B A x \]

In the configuration classes of adapter-transformers, these gating mechanisms can be activated via use_gating=True. The full UniPELT setup can be instantiated using UniPELTConfig1:

from transformers.adapters import UniPELTConfig

config = UniPELTConfig()
model.add_adapter("unipelt", config=config)

which is identical to the following ConfigUnion:

from transformers.adapters import ConfigUnion, LoRAConfig, PrefixTuningConfig, PfeifferConfig

config = ConfigUnion(
    LoRAConfig(r=8, use_gating=True),
    PrefixTuningConfig(prefix_length=10, use_gating=True),
    PfeifferConfig(reduction_factor=16, use_gating=True),
model.add_adapter("unipelt", config=config)

Finally, as the gating values for each adapter module might provide interesting insights for analysis, adapter-transformers comes with an integrated mechanism of returning all gating values computed during a model forward pass via the output_adapter_gating_scores parameter:

outputs = model(**inputs, output_adapter_gating_scores=True)
gating_scores = outputs.adapter_gating_scores

Note that this parameter is only available to base model classes and AdapterModel classes. In the example, gating_scores holds a dictionary of the following form:

    '<adapter_name>': {
        <layer_id>: {
            '<module_location>': np.array([...]),



Note that the implementation of UniPELT in adapter-transformers follows the implementation in the original code, which is slighlty different from the description in the paper. See here for more.