The two most common transfer learning techniques in NLP were feature-based transfer (generating input text embedding from a pre-trained large model and using it as a feature in your custom model) and fine-tuning (fine tuning the pre-trained model on custom data set). It is notoriously hard to fine tune Large Language Models (LLMs) for a specific task on custom domain specific dataset. Given their enormous size (e.g. GPT3 175B parameters , Google T5 Flan XXL [1] 11B parameters, Meta Llama[2] 65 billion parameters) ones needs mammoth computing horsepower and extremely large scale datasets to fine tune them on a specific task. Apart from the mentioned challenges, fine tuning LLMs on specific task may lead them to “forget” previously learnt information, a phenomena known as catastrophic forgetting.

In this blog I will provide high level overview of different Adapter[4] based parameter efficient fine tuning techniques used to fine tune LLMs. PEFT based methods make fine-tuning large language models feasible on consumer grade hardware using reasonably small datasets, e.g. Alpaca[3] used 52 k data points to fine tune Llama 7B parameter model on multiple tasks in ~3 hours using a Nvidia A100 GPU[5].

HuggingFace PEFT module has 4 types of performance efficient fine-tuning methods available under peft.PEFT_TYPE_TO_CONFIG_MAPPING

'PROMPT_TUNING': peft.tuners.prompt_tuning.PromptTuningConfig,
'PREFIX_TUNING': peft.tuners.prefix_tuning.PrefixTuningConfig,
'P_TUNING': peft.tuners.p_tuning.PromptEncoderConfig,
'LORA': peft.tuners.lora.LoraConfig

In this post I would go over theory of PROMPT_TUNING, PREFIX_TUNING and Adapter based techniques including LORA.

Before we dive into nitty-gritty of Adapter based techniques, let’s do a quick walkthrough of some other popular Additive fine tuning methods. “The main idea behind additive methods is augmenting the existing pre-trained model with extra parameters or layers and training only the newly added parameters.”[6]

1. Prompt Tuning

Prompt tuning[7] prepends the model input embeddings with a trainable tensor (known as “soft prompt”) that would learn the task specific details. The prompt tensor is optimized through gradient descent. In this approach rest of the model architecture remains unchanged.

2. Prefix-Tuning

Prefix Tuning is a similar approach to Prompt Tuning. Instead of adding the prompt tensor to only the input layer, prefix tuning adds trainable parameters are prepended to the hidden states of all layers.

Li and Liang[8] observed that directly optimizing the soft prompt leads to instabilities during training. Soft prompts are parametrized through a feed-forward network and added to all the hidden states of all layers. Pre-trained transformer’s parameters are frozen and only the prefix’s parameters are optimized.

[8] Prefix-Tuning: Optimizing Continuous Prompts for Generation

3. Overview Of Adapter Based Methodology

What are Adapters ?

As an alternative to Prompt[7] and Prefix[8] fine tuning techniques, in 2019 Houlsby[9] proposed transfer learning with Adapter modules. “Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing.”[9]. Adapters are new modules added between layers of a pre-trained network. In Adapter based learning only the new parameters are trained while the original LLM is frozen, hence we learn a very small proportion of parameters of the original LLM. This means that the model has perfect memory of previous tasks and used a small number of new parameters to learn the new task.

In [9], Houlsby highlights benefits of Adapter based techniques.

  1. Attains high performance
  2. Permits training on tasks sequentially, that is, it does not require simultaneous access to all datasets
  3. Adds only a small number of additional parameters per task.
  4. Model retains memory of previous tasks (learned during pre-training).

Tuning with adapter modules involves adding a small number of new parameters to a model, which
are trained on the downstream task. Adapter modules perform more general architectural modifications to re-purpose a pre-trained network for a downstream task. The adapter tuning strategy involves injecting new layers into the original network. The weights of the original network are untouched, whilst the new adapter layers are initialized at random.

Adapter modules have two main features:

  1. A small number of parameters
  2. Near-identity initialization.
  • A near-identity initialization is required for stable training of the adapted model
  • By initializing the adapters to a near-identity function, original network is unaffected when training starts. During training, the adapters may then be activated to change the distribution of activations throughout the network.

Adapter Modules Architecture

[9] Two Adapter Modules Inserted In a Transformer Layer (“During adapter tuning, the green layers are trained on the downstream data“)

Two serial adapters modules are inserted after each of the transformer sub-layers (Attention and Feed Forward Layers). The adapter is always applied directly to the output of the sub-layer, after the projection back to the input size, but before adding the skip connection back. The output of the adapter is then passed directly into the following layer normalization.

How Adapters Minimize Adding New Parameters?

Down Project And Up Project Matrices

Adapter modules creates a bottleneck architecture where the adapters first project (feed forward down-project weight matrix in the above image) the original d-dimensional features into a smaller dimension, m, apply a nonlinearity, then project back (feed-forward up-project weight matrix) to d dimensions. The total number of parameters added per layer, including biases, is 2m*d + d + m. By setting m << d, the number of parameters added per task are limited (less than 1%).

Given an input x, the Adapter modules output at layer l would be


  • x is a d dimensional input
  • LNl is layer normalization for the lth Adapter layer
  • Ul is feed-forward up-project m * d weight matrix
  • Dl is feed forward down-project d * m weight matrix
  • GeLU : activation funciton
  • + : residual connection

The bottleneck dimension, m, provides a simple means to trade-off performance with parameter efficiency. The adapter module itself has a skip-connection internally. With the skip-connection, if the parameters of the projection layers are initialized to near-zero, the module is initialized to an
approximate identity function. Alongside the layers in the adapter module, we also train new layer normalization parameters per task.

Pruning Adapters from lower layers

In [9] authors suggest that Adapters on the lower layers have a smaller impact than the higher-layers. Removing the adapters from the layers 0 − 4 on MNLI barely affects performance. Focusing on the upper layers is a popular strategy in fine-tuning. One intuition is that the lower layers extract lower-level features that are shared among tasks, while the higher layers build features that are unique to different tasks.

To read the complete blog (that includes LLaMA Adapters and LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS) please visit



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