Week 3: Efficient Fine-Tuning with Modern PEFT Techniques

Contents

Week 3: Efficient Fine-Tuning with Modern PEFT Techniques#

1. The Need for Parameter-Efficient Fine-Tuning (PEFT)#

The emergence of large language models (LLMs) has necessitated a new paradigm for fine-tuning. Fully fine-tuning models like GPT-3, BERT, or LLaMA with billions of parameters presents fundamental challenges:

  • Memory Explosion: A 7B parameter model alone requires ~28GB GPU memory, with actual requirements exceeding 40GB when considering gradients and optimizer states

  • Computational Cost: Updating billions of parameters consumes enormous computational resources and time

  • Overfitting Risk: Limited training data can lead to catastrophic forgetting of pre-trained knowledge

  • Storage Overhead: Storing entire models for each task becomes impractical for deployment and management

Parameter-Efficient Fine-Tuning (PEFT) solves these problems by training only a small portion of the model. The core insight is that “weight updates lie in a low-dimensional subspace”. Instead of exploring the entire parameter space, we find effective update directions.

Core Benefits of PEFT#

  • Memory Efficiency: Train 65B parameter models on a single 48GB GPU

  • Fast Convergence: Achieve 10x faster training with fewer parameters

  • Better Generalization: Constrained updates prevent overfitting and ensure stable performance

  • Modularity: Small adapters can be easily stored, shared, and swapped

  • Inference Efficiency: Merge adapters into base weights to eliminate overhead

This lecture explores cutting-edge PEFT techniques: LoRA, DoRA, WaveFT, VB-LoRA, QR-Adaptor, and QLoRA. These methods redefine efficiency boundaries, enabling researchers and practitioners to achieve maximum performance with minimal resources.

2. LoRA: The Foundation of Low-Rank Adaptation#

LoRA (Low-Rank Adaptation) has become the foundation and standard of PEFT techniques. Proposed by Microsoft in 2021, LoRA is based on the key insight that “weight updates lie in a low-dimensional subspace”, achieving efficiency through low-rank matrix decomposition instead of updating all parameters.

2.1 Core Principles of LoRA#

Instead of directly updating the full weight matrix \(W_0 \in \mathbb{R}^{d \times k}\), LoRA decomposes the update through low-rank factorization:

\[\Delta W = A \times B\]

where:

  • \(A \in \mathbb{R}^{d \times r}\) and \(B \in \mathbb{R}^{r \times k}\) are low-rank matrices

  • \(r \ll \min(d, k)\) is the rank (typically 4, 8, 16)

  • Only \(A\) and \(B\) are trainable parameters

The final weight becomes: \(W = W_0 + \Delta W = W_0 + AB\)

2.2 Mathematical Example of LoRA#

For a 768×768 attention weight matrix with rank \(r=8\):

  • Full fine-tuning: 768² = 589,824 parameters

  • LoRA: 8×(768+768) = 12,288 parameters (98% reduction!)

This dramatic parameter reduction reduces memory usage by 90%+ while minimizing performance loss.

2.3 LoRA Implementation Example#

import torch
import torch.nn as nn
from peft import LoraConfig, get_peft_model

# Apply LoRA to Korean BERT model
model_name = "klue/bert-base"
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=2,
    torch_dtype=torch.float16
)

# LoRA configuration
lora_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    r=8,                    # LoRA rank
    lora_alpha=32,          # Scaling factor
    target_modules=["query", "value", "key", "dense"],  # Target layers
    lora_dropout=0.1,
    bias="none"
)

# Apply LoRA to model
model = get_peft_model(model, lora_config)
print(f"Trainable parameters: {model.print_trainable_parameters()}")

Example Output:

trainable params: 1,572,864 || all params: 110,104,322 || trainable%: 1.43

2.4 Key Advantages and Limitations of LoRA#

Advantages:

  • Parameter Efficiency: Uses only 0.1%-0.5% of original parameters

  • Memory Savings: 90%+ reduction in memory usage

  • No Inference Overhead: Adapters can be merged into base weights

  • Modularity: Task-specific adapters can be easily swapped

Limitations:

  • Low-rank Bottleneck: Rank constraints may limit expressiveness

  • Hyperparameter Sensitivity: Performance varies with rank and alpha values

  • Lack of Layer-wise Optimization: Same settings applied to all layers

Checkpoint Questions#

  • Why does LoRA constrain weight updates to a low-dimensional subspace?

  • Calculate the parameter reduction rate for a 1024×1024 weight matrix with rank \(r=16\)

  • In what situations does LoRA’s “low-rank bottleneck” problem become more severe?

3. DoRA: High-Performance Adaptation through Weight Decomposition#

DoRA (Weight-Decomposed Low-Rank Adaptation) is an innovative PEFT technique proposed by NVIDIA in 2024 that addresses LoRA’s low-rank bottleneck problem by explicitly separating magnitude and direction of weight updates. This approach provides greater flexibility and often achieves 3.7% superior performance compared to standard LoRA.

3.1 Core Idea of DoRA#

DoRA decomposes each weight matrix \(W_0\) into two independent components:

  1. Direction: \(V = \frac{W_0}{||W_0||_F}\) (Frobenius norm normalization)

  2. Magnitude: \(m = ||W_0||_F\) (scalar magnitude)

The key insight is that these components can be updated independently during fine-tuning.

3.2 Mathematical Formulation of DoRA#

For a weight matrix \(W_0 \in \mathbb{R}^{d \times k}\):

  1. Decomposition:

    • \(V = \frac{W_0}{||W_0||_F}\) (direction vector)

    • \(m = ||W_0||_F\) (magnitude scalar)

  2. Direction Update: Apply LoRA to the direction

    • \(\Delta V = AB\) where \(A \in \mathbb{R}^{d \times r}\), \(B \in \mathbb{R}^{r \times k}\)

    • \(V' = V + \Delta V\)

  3. Magnitude Update: Learn a scaling factor

    • \(m' = m + \Delta m\) where \(\Delta m\) is a learnable scalar

  4. Reconstruction: \(W' = m' \times \frac{V'}{||V'||_F}\)

DoRA Architecture DoRA Structure: The pre-trained weight \(W_0\) is factored into a frozen direction \(V\) and a learnable magnitude \(m\). DoRA applies a LoRA-style low-rank update to adjust the direction and also tunes the magnitude \(m\). After training, the magnitude and new direction are multiplied to form the merged weight \(W'\).

3.3 Key Advantages of DoRA#

  • Decoupled Updates: Magnitude and direction can change independently

  • Better Expressiveness: Captures both scaling and directional changes

  • Minimal Overhead: Adds only a few magnitude parameters per layer

  • Drop-in Replacement: Can be used wherever LoRA is applied

3.4 DoRA Performance Results#

DoRA consistently outperforms LoRA across various benchmarks:

  • LLaMA-7B: 3.7% average improvement on commonsense reasoning tasks

  • Parameter Efficiency: Achieves better results with 25% fewer trainable parameters

  • Low-Rank Settings: Particularly effective when LoRA rank is constrained

  • Training Dynamics: Weight update patterns more closely resemble full fine-tuning

3.5 DoRA Implementation Example#

import torch
import torch.nn as nn

class DoRALayer(nn.Module):
    def __init__(self, base_layer, rank=8, alpha=32):
        super().__init__()
        self.base_layer = base_layer
        self.rank = rank
        self.alpha = alpha

        # LoRA matrices
        self.lora_A = nn.Linear(base_layer.in_features, rank, bias=False)
        self.lora_B = nn.Linear(rank, base_layer.out_features, bias=False)

        # Magnitude parameter
        self.magnitude = nn.Parameter(torch.ones(base_layer.out_features))

        # Initialize
        nn.init.kaiming_uniform_(self.lora_A.weight)
        nn.init.zeros_(self.lora_B.weight)

    def forward(self, x):
        # Base output
        base_output = self.base_layer(x)

        # LoRA update
        lora_output = self.lora_B(self.lora_A(x)) * (self.alpha / self.rank)

        # Apply magnitude scaling
        scaled_output = (base_output + lora_output) * self.magnitude

        return scaled_output

Checkpoint Questions#

  • How does DoRA’s weight decomposition differ from LoRA’s low-rank approximation?

  • Why might separating magnitude and direction updates lead to better performance?

  • Why is DoRA particularly effective in low-rank settings?

4. QLoRA: Combining 4-bit Quantization with LoRA#

QLoRA (Quantized LoRA) represents a breakthrough in efficient fine-tuning, enabling the training of 65B parameter models on a single 48GB GPU. The key innovation lies in combining 4-bit quantization with LoRA adapters while maintaining performance.

4.1 Core Concept of QLoRA#

QLoRA follows a three-step approach:

  1. Quantize: Pre-trained model weights to 4-bit precision

  2. Freeze: Quantized weights (no gradient updates)

  3. Train: LoRA adapters at 16-bit precision with full backpropagation through quantized weights

This combination reduces memory usage by ~75% while preserving model performance.

4.2 NF4 Quantization: The Key Innovation#

The success of QLoRA hinges on NF4 (NormalFloat-4), a custom 4-bit data type optimized for neural network weights:

  • Information-Theoretically Optimal: NF4 uses a logarithmic distribution that matches the normal distribution of neural weights

  • Superior Performance: Achieves 27.4 vs 31.1 perplexity compared to standard 4-bit quantization

  • Efficient Representation: Uses all 16 possible 4-bit values optimally across the weight distribution

4.3 QLoRA Technical Innovations#

Double Quantization:

  • Quantizes both model weights (4-bit) and scaling factors (8-bit)

  • Further reduces memory overhead without performance loss

  • Implemented efficiently in the bitsandbytes library

Paged Optimizers:

  • Swaps gradients and momentum to CPU memory during peaks

  • Prevents out-of-memory errors on large models

  • Enables training of models that wouldn’t fit otherwise

4.4 QLoRA Performance Results#

QLoRA achieves remarkable results:

  • Memory Efficiency: 75% reduction in memory usage

  • Performance Parity: Matches full 16-bit fine-tuning on GLUE and instruction-following tasks

  • Scalability: Enables fine-tuning of 30B-65B models on single GPUs

  • Speed: 4-bit operations are often faster than 16-bit on modern hardware

QLoRA Comparison Comparison of full fine-tuning vs LoRA vs QLoRA. QLoRA does the same low-rank adaptation but on a 4-bit quantized base model; gradients flow through the 4-bit model to the LoRA adapters. This approach cuts memory by ~75% while preserving performance.

4.5 QLoRA Implementation Example#

from transformers import BitsAndBytesConfig, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model

# Configure 4-bit quantization
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True
)

# Load model with quantization
model = AutoModelForCausalLM.from_pretrained(
    "beomi/KoAlpaca-7B",
    quantization_config=quantization_config,
    device_map="auto",
    torch_dtype=torch.float16
)

# Configure LoRA for QLoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)

# Apply LoRA
model = get_peft_model(model, lora_config)

Checkpoint Questions#

  • How does NF4 quantization differ from standard 4-bit quantization approaches?

  • What are the key technical innovations that make QLoRA work effectively?

  • When would you choose QLoRA over standard LoRA or full fine-tuning?

5. PEFT Method Comparison and Selection Guide#

Now let’s compare the LoRA, DoRA, QLoRA and other PEFT techniques we’ve explored in terms of performance, memory efficiency, and use cases.

5.1 PEFT Method Performance Comparison#

Method

Parameter Efficiency

Performance

Memory Savings

Use Case

LoRA

0.1-0.5% of model

Baseline

90%

General purpose

DoRA

0.1-0.5% of model

+3.7% over LoRA

90%

Better performance needed

QLoRA

75% memory reduction

Matches full FT

75%

Large models

VB-LoRA

0.01% of LoRA

Better than LoRA

99%

Multi-task scenarios

5.2 Situational PEFT Method Selection Guide#

For Research and Experimentation:

  • Baseline Performance: Start with LoRA

  • Better Results: Use DoRA

  • Large Models: Consider QLoRA

For Production Deployment:

  • Large Models (7B+ parameters): Use QLoRA

  • Memory-Constrained Environment: QLoRA + DoRA combination

  • Multi-Task Scenarios: Use VB-LoRA

For Resource-Limited Environments:

  • Minimal Parameter Budget: VB-LoRA

  • Memory Constraints: QLoRA

  • Storage Limitations: VB-LoRA

5.3 PEFT Method Comparison Experiment#

import time
import psutil
import torch
from typing import Dict, Any

class PEFTComparison:
    def __init__(self, model_name: str, dataset):
        self.model_name = model_name
        self.dataset = dataset
        self.results = {}

    def evaluate_method(self, method_name: str, config: Dict[str, Any]):
        """Evaluate a PEFT method and record metrics"""

        # Load model
        model = AutoModelForSequenceClassification.from_pretrained(
            self.model_name, num_labels=2
        )

        # Apply PEFT method
        if method_name == "LoRA":
            peft_config = LoraConfig(**config)
            model = get_peft_model(model, peft_config)
        elif method_name == "DoRA":
            model = apply_dora_to_model(model, **config)
        # Add other methods...

        # Record metrics
        start_time = time.time()
        start_memory = psutil.Process().memory_info().rss / 1024 / 1024  # MB

        # Training (simplified)
        trainer = Trainer(
            model=model,
            train_dataset=self.dataset,
            args=TrainingArguments(
                output_dir=f"./results/{method_name}",
                num_train_epochs=1,
                per_device_train_batch_size=8,
                logging_steps=10,
            )
        )

        trainer.train()

        end_time = time.time()
        end_memory = psutil.Process().memory_info().rss / 1024 / 1024  # MB

        # Record results
        self.results[method_name] = {
            "trainable_params": sum(p.numel() for p in model.parameters() if p.requires_grad),
            "total_params": sum(p.numel() for p in model.parameters()),
            "training_time": end_time - start_time,
            "memory_usage": end_memory - start_memory,
            "config": config
        }

        return self.results[method_name]

    def compare_methods(self):
        """Compare all methods and print results"""
        print("PEFT Methods Comparison")
        print("=" * 50)

        for method, results in self.results.items():
            print(f"\n{method}:")
            print(f"  Trainable Parameters: {results['trainable_params']:,}")
            print(f"  Parameter Ratio: {results['trainable_params']/results['total_params']:.4f}")
            print(f"  Training Time: {results['training_time']:.2f}s")
            print(f"  Memory Usage: {results['memory_usage']:.2f}MB")

Checkpoint Questions#

  • How would you choose between LoRA and DoRA for a specific task?

  • What are the key considerations when implementing QLoRA?

  • How would you design an experiment to compare PEFT methods fairly?

6. Hands-on: PEFT Method Comparison Experiment#

Now that we understand the theoretical foundations, let’s implement and compare PEFT techniques in practice. We’ll conduct a hands-on experiment comparing LoRA, DoRA, and QLoRA performance on Korean sentiment analysis.

6.1 Experiment Environment Setup#

# Install required libraries
pip install torch transformers datasets peft accelerate bitsandbytes
pip install numpy pandas scikit-learn

6.2 Korean Sentiment Analysis Dataset Preparation#

from datasets import load_dataset
from transformers import AutoTokenizer
import torch

# Load NSMC (Naver Sentiment Movie Corpus) dataset
dataset = load_dataset("nsmc")
tokenizer = AutoTokenizer.from_pretrained("klue/bert-base")

# Data preprocessing function
def preprocess_function(examples):
    return tokenizer(
        examples["document"],
        truncation=True,
        padding=True,
        max_length=128
    )

# Preprocess dataset
train_dataset = dataset["train"].map(preprocess_function, batched=True)
test_dataset = dataset["test"].map(preprocess_function, batched=True)

print(f"Training data: {len(train_dataset)} samples")
print(f"Test data: {len(test_dataset)} samples")

6.3 LoRA Implementation and Training#

from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
from peft import LoraConfig, get_peft_model, TaskType
import time

def train_lora_model():
    # Load model
    model = AutoModelForSequenceClassification.from_pretrained(
        "klue/bert-base",
        num_labels=2,
        torch_dtype=torch.float16
    )

    # LoRA configuration
    lora_config = LoraConfig(
        task_type=TaskType.SEQ_CLS,
        r=8,
        lora_alpha=32,
        target_modules=["query", "value", "key", "dense"],
        lora_dropout=0.1,
        bias="none"
    )

    # Apply LoRA to model
    model = get_peft_model(model, lora_config)
    print(f"LoRA trainable parameters: {model.print_trainable_parameters()}")

    # Training setup
    training_args = TrainingArguments(
        output_dir="./lora_results",
        num_train_epochs=3,
        per_device_train_batch_size=16,
        learning_rate=2e-4,
        logging_steps=100,
        save_steps=500,
        evaluation_strategy="steps",
        eval_steps=500,
        load_best_model_at_end=True,
    )

    # Start training
    start_time = time.time()
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset.select(range(1000)),  # Use 1000 samples for quick experiment
        eval_dataset=test_dataset.select(range(200)),
        tokenizer=tokenizer,
    )

    trainer.train()
    training_time = time.time() - start_time

    # Evaluation
    eval_results = trainer.evaluate()

    return {
        "method": "LoRA",
        "accuracy": eval_results["eval_accuracy"],
        "training_time": training_time,
        "trainable_params": sum(p.numel() for p in model.parameters() if p.requires_grad)
    }

# Execute LoRA training
lora_results = train_lora_model()
print(f"LoRA results: {lora_results}")

6.4 QLoRA Implementation and Training#

from transformers import BitsAndBytesConfig

def train_qlora_model():
    # Configure 4-bit quantization
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_use_double_quant=True,
    )

    # Load model with quantization
    model = AutoModelForSequenceClassification.from_pretrained(
        "klue/bert-base",
        num_labels=2,
        quantization_config=quantization_config,
        torch_dtype=torch.float16
    )

    # Configure LoRA for QLoRA
    lora_config = LoraConfig(
        task_type=TaskType.SEQ_CLS,
        r=8,
        lora_alpha=32,
        target_modules=["query", "value", "key", "dense"],
        lora_dropout=0.1,
        bias="none"
    )

    # Apply LoRA
    model = get_peft_model(model, lora_config)
    print(f"QLoRA trainable parameters: {model.print_trainable_parameters()}")

    # Training setup
    training_args = TrainingArguments(
        output_dir="./qlora_results",
        num_train_epochs=3,
        per_device_train_batch_size=8,  # Smaller batch size due to memory constraints
        learning_rate=2e-4,
        logging_steps=100,
        save_steps=500,
        evaluation_strategy="steps",
        eval_steps=500,
        load_best_model_at_end=True,
        fp16=True,
    )

    # Start training
    start_time = time.time()
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset.select(range(1000)),
        eval_dataset=test_dataset.select(range(200)),
        tokenizer=tokenizer,
    )

    trainer.train()
    training_time = time.time() - start_time

    # Evaluation
    eval_results = trainer.evaluate()

    return {
        "method": "QLoRA",
        "accuracy": eval_results["eval_accuracy"],
        "training_time": training_time,
        "trainable_params": sum(p.numel() for p in model.parameters() if p.requires_grad)
    }

# Execute QLoRA training
qlora_results = train_qlora_model()
print(f"QLoRA results: {qlora_results}")

6.5 Results Comparison and Analysis#

import pandas as pd
import matplotlib.pyplot as plt

def compare_results():
    # Collect results
    results = [lora_results, qlora_results]

    # Create DataFrame
    df = pd.DataFrame(results)

    # Print results
    print("PEFT Methods Comparison Results")
    print("=" * 50)
    print(df.to_string(index=False))

    # Visualization
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

    # Accuracy comparison
    ax1.bar(df['method'], df['accuracy'])
    ax1.set_title('Accuracy Comparison')
    ax1.set_ylabel('Accuracy')
    ax1.set_ylim(0.8, 1.0)

    # Training time comparison
    ax2.bar(df['method'], df['training_time'])
    ax2.set_title('Training Time Comparison')
    ax2.set_ylabel('Time (seconds)')

    plt.tight_layout()
    plt.show()

    return df

# Compare results
comparison_df = compare_results()

6.6 Experiment Results Interpretation#

Expected Results:

Method

Accuracy

Training Time

Trainable Parameters

LoRA

~0.92

~300s

~1.5M

QLoRA

~0.91

~400s

~1.5M

Key Observations:

  1. Performance: LoRA and QLoRA show similar accuracy

  2. Memory: QLoRA uses less memory but takes slightly longer to train

  3. Parameters: Both methods use the same number of trainable parameters

Checkpoint Questions#

  • Why does QLoRA take longer to train than LoRA?

  • What are the memory advantages of QLoRA?

  • Which method would you choose for a production environment?

7. PEFT Techniques in Practice and Future Prospects#

In this final section, we’ll explore practical applications of PEFT techniques and look toward future developments in the field.

7.1 Practical Application Guide by PEFT Method#

LoRA Applications:

  • General fine-tuning: Most NLP tasks with moderate resource requirements

  • Multi-task learning: Easy adapter swapping for different tasks

  • Research prototyping: Quick experimentation with different configurations

DoRA Applications:

  • Performance-critical tasks: When you need better results than LoRA

  • Low-rank constraints: When memory is extremely limited

  • Production systems: Where performance improvement justifies complexity

QLoRA Applications:

  • Large model fine-tuning: 7B+ parameter models on single GPUs

  • Memory-constrained environments: Limited GPU memory scenarios

  • Research with large models: Experimenting with state-of-the-art models

7.2 Comprehensive PEFT Performance Comparison#

Method

Parameter Efficiency

Performance

Memory Savings

Training Speed

Use Case

LoRA

0.1-0.5% of model

Baseline

90%

Fast

General purpose

DoRA

0.1-0.5% of model

+3.7% over LoRA

90%

Fast

Better performance needed

QLoRA

75% memory reduction

Matches full FT

75%

Medium

Large models

VB-LoRA

0.01% of LoRA

Better than LoRA

99%

Fast

Multi-task scenarios

7.3 Practical Implementation Considerations#

Memory Optimization:

# Enable gradient checkpointing
training_args = TrainingArguments(
    gradient_checkpointing=True,
    dataloader_pin_memory=False,
    dataloader_num_workers=0,
)

# Use mixed precision
training_args = TrainingArguments(
    fp16=True,  # or bf16=True for newer GPUs
)

Hyperparameter Tuning:

# LoRA hyperparameters
lora_configs = [
    {"r": 4, "lora_alpha": 16},   # Minimal parameters
    {"r": 8, "lora_alpha": 32},   # Balanced
    {"r": 16, "lora_alpha": 64},  # High capacity
]

# Target modules selection
target_modules_options = [
    ["query", "value"],                    # Attention only
    ["query", "value", "key"],             # Full attention
    ["query", "value", "key", "dense"],    # Attention + FFN
]

7.4 Future Directions in PEFT#

The field of PEFT is rapidly evolving. Key areas of future development include:

  1. Automated PEFT Selection: AI-driven methods to automatically choose the best PEFT technique for a given task and constraints.

  2. Dynamic Adaptation: Methods that can adjust their parameter efficiency during training based on task complexity.

  3. Cross-Modal PEFT: Extending PEFT techniques to multimodal models (vision-language, audio-text).

  4. Hardware-Aware PEFT: Techniques that are specifically optimized for different hardware configurations (mobile, edge, cloud).

  5. Federated PEFT: Distributed fine-tuning where different clients use different PEFT methods based on their local constraints.

7.5 Practical Recommendations#

  1. Start Simple: Begin with LoRA for most tasks, then explore more advanced methods as needed.

  2. Profile Your Constraints: Understand your memory, compute, and storage limitations before choosing a method.

  3. Experiment Systematically: Use the comparison framework provided to evaluate different methods on your specific task.

  4. Stay Updated: The PEFT field is rapidly evolving, with new methods being published regularly.

  5. Consider the Full Pipeline: Factor in not just training efficiency, but also deployment, storage, and inference considerations.

Checkpoint Questions#

  • How would you choose the optimal PEFT method for a specific production scenario?

  • What are the key trade-offs between different PEFT techniques?

  • How do you see PEFT techniques evolving in the next few years?

References#

Key Papers and Research Materials#

  1. LoRA: Low-Rank Adaptation of Large Language Models

  2. DoRA: Weight-Decomposed Low-Rank Adaptation

  3. QLoRA: Efficient Finetuning of Quantized LLMs

  4. Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets

Technical Documentation and Implementations#

  1. PEFT: Parameter-Efficient Fine-Tuning Methods for LLMs

  2. bitsandbytes: 8-bit optimizers and quantization routines

  3. DoRA Implementation

Online Resources and Blogs#

  1. Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

  2. PEFT Methods Explained

  3. VB-LoRA Documentation

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