PEFT for LLMs#
Large Language Models (LLMs) have revolutionized the field of natural language processing. Their vast sizes, ranging from millions to billions of parameters, present considerable challenges in computational resources and training costs. For instance, loading a 70 billion parameter model necessitates an immense 280 GB of GPU memory. The extensive resources and high expenses required for training these models underscore the need for more efficient methodologies. ParameterEfficient FineTuning (PEFT) emerges as a solution, offering techniques to adapt segments of these colossal models to specific tasks, thereby reducing computational burdens.
In this context, the necessity for more efficient approaches to leverage these models is clear, especially for those with limited datasets and computational resources. This is where ParameterEfficient FineTuning (PEFT) comes into play. PEFT offers a suite of techniques that enable the adaptation of small sections of these massive models to specific tasks, thereby reducing the overall computational burden.
This lecture aims to provide a comprehensive understanding of each PEFT technique currently implemented in Hugging Face, delineating the differences among them. This guide fills the gaps identified in these materials and serves as a conceptual foundation for all PEFT methods present in Hugging Face. The goal is to equip readers with a fundamental understanding of PEFT, facilitating their engagement with research literature on additional PEFT techniques.
Standard FineTuning vs. PEFT#
Finetuning Large Language Models (LLMs) is a complex process with implications for model performance, data security, and resource utilization. The choice between standard finetuning and ParameterEfficient FineTuning (PEFT) depends on various factors, including data availability, computational resources, and specific usecase requirements.
Data Security and Privacy Concerns#
Data Retention in LLMs: Research indicates that LLMs can retain a fraction of their training data. The risk of data retention increases with data duplication, leading to potential privacy and security issues, especially when models are externally accessible.
Prompt Injection Attacks: One significant risk associated with finetuned LLMs is prompt injection attacks, where malicious inputs can trigger the model to reveal sensitive training data.
InContext Learning (ICL) as an Alternative: For scenarios where data security is paramount, ICL, which involves dynamic observation selection, can be a safer alternative. It allows the model to adapt to new tasks without explicit finetuning, reducing the risk of data leakage.
Importance of Data Quality#
Impact on FineTuning Success: The success of both standard and PEFT largely depends on the quality of data labels. Poorly labeled data can lead to ineffective finetuning, regardless of the method used.
Data Labeling Commitment: Highquality data labeling is crucial, particularly for standard finetuning, which involves a more extensive modification of the model.
Comparative Analysis#
Parameterefficient Finetuning 
Standard Finetuning 


Goal 
Tailor a pretrained model to specific tasks efficiently with limited data and computational resources. 
Enhance model performance on specific tasks using comprehensive training with ample data and resources. 
Training Data 
Optimal for scenarios with limited datasets, typically requiring fewer examples. 
Requires large datasets with numerous examples for effective training. 
Training Time 
Generally offers a quicker training process due to fewer parameters being tuned. 
Involves a longer training duration due to the extensive tuning of all model parameters. 
Computational Resources 
More resourceefficient, utilizing fewer computational resources. 
Demands significant computational resources for processing large models and datasets. 
Model Parameters 
Involves modifying a small, specific subset of the model’s parameters. 
Entails retraining the entire model, adjusting all parameters. 
Overfitting 
Reduced risk of overfitting due to limited modifications to the model. 
Higher potential for overfitting as the model undergoes extensive modifications. 
Training Performance 
Achieves good performance, although it may not match the levels attained by full finetuning. 
Typically yields better performance, optimizing the model’s capabilities to the fullest. 
Use Cases 
Suitable for applications in lowresource settings or when extensive training data isn’t available. 
Ideal for wellresourced environments with access to large amounts of training data. 
Selecting between standard finetuning and PEFT involves balancing various factors. Standard finetuning is more resourceintensive but can lead to higher model performance, making it suitable for scenarios with sufficient computational resources and data. In contrast, PEFT is ideal for situations where resources are limited or when working with smaller datasets. It offers a practical way to leverage the capabilities of large models without the extensive resource requirements of standard finetuning. Additionally, considerations of data privacy and security are crucial in deciding the most appropriate approach for finetuning LLMs.
Who Uses PEFT?#
ParameterEfficient FineTuning (PEFT) has garnered significant attention and adoption across various sectors of the artificial intelligence and machine learning industry, especially among entities that specialize in language model services. The appeal of PEFT lies in its ability to adapt largescale pretrained language models (PLMs) to specific tasks with minimal resource overhead.
Current Adoption of PEFT#
Hugging Face 🤗#
Wide Range of PEFT Techniques: Hugging Face, a prominent player in the field of AI and NLP, offers an extensive suite of PEFT techniques through its platform. This includes various innovative methods such as LoRA, Prefix Tuning, and PTuning, among others.
OpenSource Accessibility: Hugging Face’s commitment to opensource technology has made PEFT techniques more accessible to the wider AI community, allowing researchers and developers to experiment with and implement these methods in their projects.
Community Contributions: The active community around Hugging Face contributes to the continuous development and refinement of PEFT methods, ensuring they stay at the forefront of AI research and application.
Google’s Vertex AI#
Reference to PEFT Research: Google’s Vertex AI, known for providing advanced machine learning services, references the survey by Liali et al. on PEFT in its tuning guide.
Opaque Implementation Details: While Google acknowledges the significance of PEFT, the specifics of how Vertex AI implements these techniques are not fully disclosed. This opacity leaves room for speculation about the extent and manner of PEFT’s integration into Google’s AI offerings.
OpenAI#
Current NonImplementation: As per a nowremoved blog post, OpenAI, renowned for its GPT models, has not yet incorporated PEFT methods into its suite of tools and services.
Potential Future Adoption: Given OpenAI’s reputation for pioneering AI research and development, it is anticipated that PEFT will eventually be part of its toolset. The adoption of PEFT by OpenAI could be driven by the increasing demand for efficient finetuning methods and the need to make large models like GPT more accessible and adaptable.
Quick Transformer Review#
The Transformer architecture, introduced by Vaswani et al. in their seminal paper “Attention Is All You Need,” has become a cornerstone in modern natural language processing (NLP) and machine learning. This architecture is fundamental to understanding the nuances of ParameterEfficient FineTuning (PEFT) techniques.
Core Concepts of Transformer Architecture#
SelfAttention Mechanism#
Principle: At the heart of the Transformer is the selfattention mechanism, which enables the model to weigh the importance of different words in a sentence for a given task. Unlike previous architectures that processed words in sequence, the Transformer treats input data in parallel, allowing it to capture complex word relationships more effectively.
Functionality: Selfattention works by creating three vectors for each input token (word): the query vector, key vector, and value vector. These vectors are derived through learned linear transformations.
Attention Calculation: The attention scores are computed by taking the dot product of the query vector with key vectors of all tokens and applying a softmax function. These scores determine how much focus to put on other parts of the input sentence as each word is processed.
Scaled DotProduct Attention: The scores are then multiplied with the value vectors and summed up, resulting in the output for each token. This process is scaled by the square root of the dimension of key vectors to prevent gradients from vanishing or exploding.
Transformer Block Structure#
Layers: A standard transformer block consists of two main layers: the multihead selfattention layer and the positionwise feedforward network.
MultiHead Attention: This layer extends the selfattention mechanism by having multiple ‘heads’, each performing attention calculations independently. The outputs of these heads are then concatenated and linearly transformed into the desired dimension. This multihead approach allows the model to attend to information from different representation subspaces at different positions.
FeedForward Network: Following the attention layer, the output passes through a positionwise feedforward network, typically comprising two linear transformations with an activation function in between.
Normalization and Residual Connections: Each of these layers is followed by layer normalization and is equipped with residual connections. This means the output of each layer is the sum of its input and its processed result, enhancing training stability and allowing deeper networks.
Positional Encoding: Since the Transformer doesn’t inherently capture the sequential nature of data, positional encodings are added to the input embeddings to give the model information about the position of each token in the sequence.
def self_attention(x):
# Project the input to create key (k), query (q), and value (v) matrices
k = x @ W_k # Key matrix is obtained by multiplying the input with weight W_k
q = x @ W_q # Query matrix is obtained by multiplying the input with weight W_q
v = x @ W_v # Value matrix is obtained by multiplying the input with weight W_v
# Calculate the attention scores and apply them to the value matrix
# Softmax is applied to the dot product of q and the transpose of k for normalization
# The resulting attention scores are then multiplied with the value matrix v
return softmax(q @ k.T) @ v
def transformer_block(x):
# Store the original input for the residual connection
residual = x
# Apply selfattention to the input
x = self_attention(x)
# Add the residual (original input) to the output of the selfattention layer
# and apply layer normalization
x = layer_norm(x + residual)
# Store the output of the first sublayer for the next residual connection
residual = x
# Apply the FeedForward Network (FFN) to the output of the first sublayer
x = FFN(x)
# Add the residual (output of the first sublayer) to the output of the FFN
# and apply layer normalization
x = layer_norm(x + residual)
# Return the output of the transformer block
return x
In this code snippet, the self_attention
function defines the selfattention mechanism used in transformer models, projecting input x
into key, query, and value matrices and applying the attention mechanism. The transformer_block
function represents a single block of a transformer model, which includes a selfattention layer followed by a feedforward network (FFN), both supplemented with residual connections and layer normalization for stability and performance improvement.
Importance in PEFT#
PEFT Adaptations: Understanding the Transformer’s architecture is crucial for comprehending how PEFT techniques modify it. PEFT methods typically intervene at the level of the selfattention mechanism and the feedforward networks, adjusting or augmenting the model’s parameters efficiently for taskspecific finetuning.
Targeting Specific Components: Depending on the PEFT technique used, modifications may be made to specific components like the attention heads or layers of the feedforward network, enabling the finetuning process to be both efficient and effective with fewer trainable parameters.
PEFT Techniques Overview#
Additive Methods#
Adapters: Introduced by Houlsby et al., this technique involves adding small fully connected networks after Transformer sublayers. The pseudo code for an adapted transformer block illustrates these additions.
def transformer_block_adapter(x):
# Store the original input for the residual connection
residual = x
# Apply selfattention to the input
x = self_attention(x)
# Apply the first FeedForward Network (FFN), which acts as an adapter in this context
# Adapters are small networks added after the transformer sublayers
x = FFN(x) # Adapter layer
# Add the residual (original input) to the output of the adapter layer
# and apply layer normalization
x = layer_norm(x + residual)
# Store the output of the first sublayer (including the adapter) for the next residual connection
residual = x
# Apply the second FeedForward Network (FFN) for further transformation
x = FFN(x)
# Apply another adapter after the second FFN
x = FFN(x) # Second adapter layer
# Add the residual (output of the first adapter layer) to the output of the second adapter layer
# and apply layer normalization
x = layer_norm(x + residual)
# Return the output of the transformer block with adapters
return x
In this code snippet, the transformer_block_adapter
function represents a transformer block modified to include adapters, as introduced by Houlsby et al. Adapters are additional small feedforward networks inserted after each sublayer within the transformer block (after selfattention and the original FFN). These adapters provide a means to finetune the pretrained transformer models efficiently by only training the parameters within these adapter layers, rather than the entire model. This approach helps in adapting the model to specific tasks while keeping the computational overhead low.
(IA)³: This method, proposed by Liu et al., augments the transformer block with additional column vectors (\(l_k\), \(l_v\)) that modify the key and value matrices in the attention mechanism. This is done without strictly adding fully connected layers, distinguishing it from traditional adapter methods.
def self_attention_ia3(x):
# Project the input to create key (k), query (q), and value (v) matrices
k = x @ W_k # Key matrix is obtained by multiplying the input with weight W_k
q = x @ W_q # Query matrix is obtained by multiplying the input with weight W_q
v = x @ W_v # Value matrix is obtained by multiplying the input with weight W_v
# IA³ augmentation: Modify the key and value matrices using additional column vectors l_k and l_v
# These additional vectors allow for specific adjustments to the attention mechanism
k = l_k @ k # Modify key matrix with l_k vector
v = l_v @ v # Modify value matrix with l_v vector
# Calculate the attention scores and apply them to the value matrix
# Softmax is applied to the dot product of q and the transpose of k for normalization
# The resulting attention scores are then multiplied with the value matrix v
return softmax(q @ k.T) @ v
def transformer_block_ia3(x):
# Store the original input for the residual connection
residual = x
# Apply the IA³augmented selfattention to the input
x = self_attention_ia3(x)
# Add the residual (original input) to the output of the selfattention layer
# and apply layer normalization
x = layer_norm(x + residual)
# Store the output of the first sublayer for the next residual connection
residual = x
# Apply the first part of the FeedForward Network (FFN)
x = x @ W_1 # Normal transformer feedforward operation
# IA³ augmentation for the FeedForward Network
# Apply an elementwise multiplication with the l_ff vector after applying the GELU activation function
# This step introduces adaptability specific to the IA³ approach
x = l_ff * gelu(x) # Modify FFN output with l_ff vector
# Complete the FFN operation
x = x @ W_2
# Add the residual (output of the first sublayer) to the output of the FFN
# and apply layer normalization
x = layer_norm(x + residual)
# Return the output of the transformer block with IA³ modifications
return x
In this code snippet, the self_attention_ia3
function and the transformer_block_ia3
function represent the IA³ method as proposed by Liu et al. The IA³ method augments the standard Transformer architecture by introducing additional column vectors to modify the key and value matrices in the selfattention mechanism, as well as applying modifications to the feedforward network. This approach allows for targeted adjustments to the model’s attention and feedforward mechanisms, enhancing its adaptability for specific tasks without adding fully connected layers typical of traditional adapter methods.
SoftPrompts#
PromptTuning: Developed by Lester et al., this technique involves creating a set of parameters for prompt tokens and integrating them at the beginning of the network. It allows for optimization of a continuous representation of the prompt text.
def prompt_tuning(seq_tokens, prompt_tokens):
# Create embeddings for the input sequence tokens
# seq_embedding is a function that transforms sequence tokens into their corresponding embeddings
x = seq_embedding(seq_tokens)
# Create soft prompt embeddings
# prompt_embedding is a function that creates embeddings for a set of trainable prompt tokens
# These prompt tokens are not part of the original input but are learned parameters that are optimized during training
soft_prompt = prompt_embedding(prompt_tokens)
# Concatenate the soft prompt embeddings with the input sequence embeddings
# This operation combines the prompt information with the actual input, allowing the model to consider the prompt context
# 'dim=seq' specifies the dimension along which the concatenation occurs, typically the sequence length dimension
model_input = concat([soft_prompt, x], dim=seq)
# Pass the concatenated input through the model
# The model processes the combined prompt and sequence input, utilizing the prompt context for better adaptation to the task
return model(model_input)
In this code snippet, prompt_tuning
function represents the PromptTuning technique as developed by Lester et al. This approach involves integrating a set of learned prompt tokens at the beginning of the input sequence. These prompt tokens, represented as “soft prompts,” are not fixed but are trainable parameters that are optimized during the training process. By concatenating these soft prompt embeddings with the input sequence embeddings, the model is provided with additional context or guidance, enhancing its ability to adapt to specific tasks or datasets. This method allows for the finetuning of large language models in a parameterefficient manner, as only the prompt embeddings are trained, leaving the rest of the model’s parameters frozen.
Prefix Tuning: Similar to prompt tuning but differs in that the representation is fed to all layers of the transformer. It also involves learning additional parameters for the soft prompt in the form of a fully connected network.
def transformer_block_prefix_tuning(x, soft_prompt):
# Apply a FeedForward Network (FFN) to the soft prompt
# The FFN is used to transform the soft prompt embeddings, allowing for more complex and adaptable representations
# This FFN is part of the learnable parameters and is specific to the prefix tuning method
soft_prompt = FFN(soft_prompt)
# Concatenate the transformed soft prompt embeddings with the input sequence embeddings
# Unlike prompt tuning where the prompt is only added at the beginning, in prefix tuning
# the transformed soft prompt is designed to be integrated with every layer of the transformer
# 'dim=seq' specifies the dimension along which the concatenation occurs, typically the sequence length dimension
model_input = concat([soft_prompt, x], dim=seq)
# Pass the concatenated input (including the soft prompt) through the model
# The transformer model processes the input sequence along with the embedded prompts,
# allowing each layer of the transformer to utilize the prompt information
return model(model_input)
In this code snippet, the transformer_block_prefix_tuning
function represents the Prefix Tuning technique. Prefix Tuning is similar to Prompt Tuning in that it involves adding soft prompts to the input. However, it differs significantly in its approach to integrating these prompts. In Prefix Tuning, the soft prompts are first transformed by a FeedForward Network (FFN), and this transformed representation is then concatenated with the input sequence embeddings. This concatenated input is fed to every layer of the transformer model, as opposed to just the beginning of the model in standard Prompt Tuning. This method allows for a more extensive and integrated use of the prompt information throughout the model, potentially leading to more nuanced and effective adaptations to specific tasks or datasets.
PTuning: Proposed by Liu et al., PTuning encodes the prompt using an LSTM, aiming to address the discrete nature of word embeddings and their independent associations in other prompting methods.
def p_tuning(seq_tokens, prompt_tokens):
# Create embeddings for the prompt tokens
# prompt_embedding is a function that transforms prompt tokens into their corresponding embeddings
# These prompt tokens are learnable parameters that are optimized during training
h = prompt_embedding(prompt_tokens)
# Process the prompt embeddings with a bidirectional LSTM
# The LSTM (Long ShortTerm Memory) network is used to capture sequential information in the prompts
# and can model dependencies in both forward and reverse directions (bidirectional)
h = LSMT(h, bidirectional=True)
# Apply a FeedForward Network (FFN) to the output of the LSTM
# This step further transforms the prompt embeddings, allowing for richer representations
h = FFN(h)
# Create embeddings for the input sequence tokens
# seq_embedding is a function that transforms sequence tokens into their corresponding embeddings
x = seq_embedding(seq_tokens)
# Concatenate the transformed prompt embeddings (h) with the input sequence embeddings (x)
# This operation combines the prompt information with the actual input sequence
# 'dim=seq' specifies the dimension along which the concatenation occurs, typically the sequence length dimension
model_input = concat([h, x], dim=seq)
# Pass the concatenated input (including the prompt embeddings) through the model
# The transformer model processes the input sequence along with the embedded prompts
return model(model_input)
In this code snippet, the p_tuning
function represents the PTuning technique as proposed by Liu et al. PTuning aims to address the limitations of other prompting methods by using an LSTM network to encode the prompts. This approach helps to capture the sequential nature and dependencies within the prompt embeddings more effectively. The prompt embeddings are first transformed by the LSTM and then further processed by a FeedForward Network (FFN) to enrich their representation. These transformed prompt embeddings are then concatenated with the input sequence embeddings, and the combined input is fed into the model. This method allows for a more nuanced integration of the prompt information into the model, potentially leading to more effective adaptations for specific tasks.
LLaMAAdapter: As per Zhang et al., this technique applies a variant of prefix learning to the Llama model. It introduces adaptation prompts and zeroinitialized attention for efficient finetuning.
def transformer_block_llama_adapter(x, soft_prompt, gating_factor):
# Store the original input for the residual connection
residual = x
# Create an adaptation prompt by concatenating the soft prompt with the input
# This adaptation prompt is a modification specific to the LLaMAAdapter approach
adaption_prompt = concat([soft_prompt, x], dim=seq)
# Apply selfattention to the adaptation prompt
# The selfattention mechanism processes the combined input of soft prompts and sequence tokens
adaption_prompt = self_attention(adaption_prompt)
# Apply gating to the adaptation prompt using a zeroinitialized attention mechanism
# This gating factor controls the influence of the adaptation prompt on the transformer block
# The zeroinit attention helps in starting the training from a state where the adaptation prompt has minimal impact,
# gradually learning its influence during training
adaption_prompt = adaption_prompt * gating_factor
# Apply selfattention to the original input sequence
x = self_attention(x)
# Combine the output of the selfattention with the adapted prompt
# The elementwise multiplication integrates the adaptation prompt into the main data flow
x = adaption_prompt * x
# Add the residual (original input) to the output of the combined selfattention and adaptation prompt
# and apply layer normalization
x = layer_norm(x + residual)
# Store the output of the first sublayer for the next residual connection
residual = x
# Apply the FeedForward Network (FFN) to the output of the first sublayer
x = FFN(x)
# Add the residual (output of the first sublayer) to the output of the FFN
# and apply layer normalization
x = layer_norm(x + residual)
# Return the output of the transformer block with the LLaMAAdapter modifications
return x
In this code snippet, the transformer_block_llama_adapter
function represents the implementation of the LLaMAAdapter technique. This method applies a variant of prefix learning to the transformer model, incorporating adaptation prompts and a zeroinitialized attention mechanism. The adaptation prompt, created by combining soft prompts with the input sequence, is processed through selfattention and then modulated by a gating factor. This approach allows for efficient finetuning by introducing an adaptable mechanism that initially has minimal impact but learns to influence the transformer block’s processing over the course of training. The LLaMAAdapter thus provides a novel way to finetune transformer models, enhancing their adaptability to specific tasks while maintaining the underlying model structure.
ReparameterizationBased Methods#
LoRa: A popular technique by Hu et al., LoRa reparameterizes a weight matrix by learning a separate matrix representing updates from optimization. It uses two smaller matrices to represent these updates, reducing the number of parameters to be learned.
def lora_linear(x, W):
# Scale factor based on the rank r
# In LoRa, a lowrank approximation is used to reduce the number of learnable parameters
# r is the rank, which is a hyperparameter determining the size of the lowrank matrices W_a and W_b
scale = 1 / r
# Standard linear transformation using the original weight matrix W
h = x @ W
# LoRa modification: Apply lowrank linear transformation
# W_a and W_b are smaller matrices representing updates from optimization
# The product of W_a and W_b approximates the updates to the original weight matrix W
# This approach reduces the number of parameters to learn, focusing on the most impactful parts of W
h += x @ W_a @ W_b
# Scale the result of the transformation
# The scaling helps in balancing the influence of the lowrank approximation
return scale * h
def self_attention_lora(x):
# Apply the LoRamodified linear transformation to the key and value matrices
# LoRa reparameterizes the original weight matrices (W_k and W_v) of the key and value vectors
k = lora_linear(x, W_k) # LoRa applied to key matrix
q = x @ W_q # Standard linear transformation for query matrix
v = lora_linear(x, W_v) # LoRa applied to value matrix
# Calculate the attention scores and apply them to the value matrix
# The attention mechanism remains the same as in the standard selfattention
# The key and value matrices are modified by LoRa, allowing for efficient learning of the most impactful parameters
return softmax(q @ k.T) @ v
In this code snippet, the lora_linear
function implements the LowRank Adaptation (LoRa) technique for linear layers, and the self_attention_lora
function integrates this technique into the selfattention mechanism of a transformer. LoRa focuses on reparameterizing the weight matrices of the key and value vectors using lowrank matrices. By learning updates in the form of smaller matrices (W_a and W_b), LoRa efficiently captures the most significant changes to the weights while reducing the overall number of parameters that need to be learned. This approach maintains the core functionality of the selfattention mechanism while allowing for more efficient and focused training, particularly beneficial for adapting large pretrained models to new tasks or datasets.
Selective Methods#
AdaLoRa: This hybrid approach, developed by Zhang et al., combines ideas from reparameterization and selective methods. It uses an approximation of Singular Value Decomposition (SVD) to represent weight matrix updates and incorporates a pruning technique to eliminate less important singular vectors.
def adalora_linear(x, W, curr_sv):
# Scale factor based on the rank r
# In AdaLoRa, a lowrank approximation with a scaling factor is used
# r is the rank and alpha is a scaling hyperparameter
scale = alpha / r
# Standard linear transformation using the original weight matrix W
h = x @ W
# AdaLoRa modification: Apply lowrank linear transformation using SVD components
# p, lambda, and q are matrices derived from the singular value decomposition (SVD) of W
# curr_sv represents the current singular vectors being optimized
# This approach selectively updates the weight matrix W focusing on its most significant singular vectors
h += x @ p[curr_sv] @ lamda[curr_sv] @ q[curr_sv]
# Scale the result of the transformation
return scale * h
def self_attention_adalora(x):
"""
AdaLoRaspecific selfattention mechanism.
This function shows how AdaLoRa is integrated into the selfattention block.
It does not include the pruning techniques used in AdaLoRa.
"""
# Apply the AdaLoRamodified linear transformation to the key and value matrices
# AdaLoRa reparameterizes the original weight matrices (W_k and W_v) of the key and value vectors
k = adalora_linear(x, W_k) # AdaLoRa applied to key matrix
q = x @ W_q # Standard linear transformation for query matrix
v = adalora_linear(x, W_v) # AdaLoRa applied to value matrix
# Calculate the attention scores and apply them to the value matrix
# The attention mechanism remains the same as in standard selfattention
# The key and value matrices are modified by AdaLoRa, focusing on efficient learning of significant parameters
return softmax(q @ k.T) @ v
In this code snippet, the adalora_linear
function implements the AdaLoRa technique for linear layers, and the self_attention_adalora
function integrates AdaLoRa into the selfattention mechanism of a transformer. AdaLoRa combines ideas from reparameterization with selective methods, using an approximation of Singular Value Decomposition (SVD) to represent weight matrix updates. This method focuses on optimizing the most significant components of the weight matrices, identified through SVD, and incorporates a scaling factor for effective learning. By selectively updating these key components, AdaLoRa provides an efficient way to finetune large pretrained models, targeting the adjustments that have the most substantial impact on model performance. This approach is particularly beneficial for resourceefficient training and adapting models to new tasks while maintaining their underlying structure.
Comparison of Methods#
The table below provides a comparison of these methods, highlighting the number of trainable parameters, method type, and a brief summary.
Method 
Trainable Parameters 
Type 
Summary 

Adapters 
Low 
Additive 
Adds small networks after sublayers. 
(IA)³ 
Low 
Additive 
Augments attention mechanism with additional vectors. 
PromptTuning 
Very Low 
SoftPrompts 
Optimizes a continuous representation of the prompt text. 
Prefix Tuning 
Low 
SoftPrompts 
Feeds prompt representation to all transformer layers. 
PTuning 
Low 
SoftPrompts 
Encodes prompts using an LSTM. 
LLaMAAdapter 
Low 
SoftPrompts 
Applies efficient prefix learning to Llama model. 
LoRa 
Very Low 
Reparameterization 
Reparameterizes weight matrix with two smaller matrices. 
AdaLoRa 
Low 
Selective/Hybrid 
Uses SVD approximation for weight matrix updates. 
Conclusion#
ParameterEfficient FineTuning (PEFT) stands as a transformative approach in the realm of working with Large Language Models (LLMs), addressing the critical challenges of computational demand and resource constraints. By focusing on adapting smaller segments of these expansive models, PEFT offers a path to harness the power of LLMs in a more accessible and sustainable manner. This approach not only broadens the scope of who can utilize these models but also expands the potential applications in various fields.
The integration of PEFT techniques, particularly those developed and made accessible by platforms like Hugging Face, marks a significant advancement in the field of natural language processing and artificial intelligence. These techniques allow for the efficient customization of LLMs for specific tasks, providing a balance between performance and resource utilization. The diverse range of PEFT methods, including LoRA, Prefix Tuning, and AdaLoRA, each offers unique advantages, catering to different requirements and scenarios.
The potential of PEFT extends beyond just language models; its principles are being applied to other areas such as diffusion models, indicating a broad spectrum of impact. Whether it’s finetuning for specific tasks, adapting models for resourcelimited settings, or pushing the boundaries of what’s possible with consumergrade hardware, PEFT is at the forefront of this evolution.
In conclusion, PEFT represents a significant step forward in the democratization of AI and ML technologies. It opens up new avenues for innovation, allowing a wider range of researchers, developers, and organizations to leverage the capabilities of largescale models in a more efficient and effective manner. As the field continues to evolve, the role of PEFT in shaping the future of machine learning and AI will undoubtedly be pivotal, driving forward the possibilities of what can be achieved with these powerful tools.