Using LoRA for Fine-tuning

LoRA (Low-Rank Adaptation) enables efficient fine-tuning by freezing pretrained weights and injecting small trainable matrices into transformer layers. This reduces trainable parameters to ~0.1% of the original model while maintaining performance.

Table of Contents

• Core Concepts
• Basic Setup
• Configuration Parameters
• QLoRA (Quantized LoRA)
• Training Patterns
• Saving and Loading
• Merging Adapters
• Best Practices
• References

Core Concepts

How LoRA Works

Instead of updating all weights during fine-tuning, LoRA decomposes weight updates into low-rank matrices:

W' = W + BA

Where:

• W is the frozen pretrained weight matrix (d × k)
• B is a trainable matrix (d × r)
• A is a trainable matrix (r × k)
• r is the rank, much smaller than d and k

The key insight: weight updates during fine-tuning have low intrinsic rank, so we can represent them efficiently with smaller matrices.

Why Use LoRA

Basic Setup

Installation

pip install peft transformers accelerate

Minimal Example

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import LoraConfig, get_peft_model, TaskType
import torch

# Load base model
model_name = "meta-llama/Llama-3.2-1B"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

# Configure LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM,
)

# Apply LoRA
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# trainable params: 3,407,872 || all params: 1,238,300,672 || trainable%: 0.28%

Configuration Parameters

LoraConfig Options

from peft import LoraConfig, TaskType

config = LoraConfig(
    # Core parameters
    r=16,                          # Rank of update matrices
    lora_alpha=32,                 # Scaling factor (alpha/r applied to updates)
    target_modules=["q_proj", "v_proj"],  # Layers to adapt

    # Regularization
    lora_dropout=0.05,             # Dropout on LoRA layers
    bias="none",                   # "none", "all", or "lora_only"

    # Task configuration
    task_type=TaskType.CAUSAL_LM,  # CAUSAL_LM, SEQ_CLS, SEQ_2_SEQ_LM, TOKEN_CLS

    # Advanced
    modules_to_save=None,          # Additional modules to train (e.g., ["lm_head"])
    layers_to_transform=None,      # Specific layer indices to adapt
    rank_pattern=None,             # Per-module rank overrides
    alpha_pattern=None,            # Per-module alpha overrides
    trainable_token_indices=None,  # Train only selected new token embeddings
    target_parameters=None,        # Target nn.Parameter weights in some MoE layers
    use_rslora=False,              # Rank-stabilized LoRA scaling
    use_dora=False,                # Weight-Decomposed LoRA
)

Target Modules by Architecture

# Llama, Mistral, Qwen
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

# GPT-2, GPT-J
target_modules = ["c_attn", "c_proj", "c_fc"]

# BERT, RoBERTa
target_modules = ["query", "key", "value", "dense"]

# Falcon
target_modules = ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"]

# Phi
target_modules = ["q_proj", "k_proj", "v_proj", "dense", "fc1", "fc2"]

# MoE expert parameters stored as nn.Parameter tensors
target_parameters = ["feed_forward.experts.gate_up_proj", "feed_forward.experts.down_proj"]

For MoE models where expert weights are not nn.Linear modules, use target_parameters rather than target_modules. Merge adapters before latency-sensitive inference because materializing expert LoRA updates can add overhead.

Finding Target Modules

# Print all linear layer names
from peft.utils import get_peft_model_state_dict

def find_target_modules(model):
    linear_modules = set()
    for name, module in model.named_modules():
        if isinstance(module, torch.nn.Linear):
            # Get the last part of the name (e.g., "q_proj" from "model.layers.0.self_attn.q_proj")
            layer_name = name.split(".")[-1]
            linear_modules.add(layer_name)
    return list(linear_modules)

print(find_target_modules(model))

QLoRA (Quantized LoRA)

QLoRA combines 4-bit quantization with LoRA, enabling fine-tuning of large models on consumer GPUs.

Setup

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

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",           # Normalized float 4-bit
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,       # Nested quantization
)

# Load quantized model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.2-3B",
    quantization_config=bnb_config,
    device_map="auto",
)

# Prepare for k-bit training
model = prepare_model_for_kbit_training(model)

# Apply LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type=TaskType.CAUSAL_LM,
)

model = get_peft_model(model, lora_config)

Memory Requirements

Training Patterns

With Hugging Face Trainer

from transformers import TrainingArguments, Trainer, DataCollatorForLanguageModeling
from datasets import load_dataset

# Prepare dataset
dataset = load_dataset("tatsu-lab/alpaca", split="train")

def format_prompt(example):
    if example["input"]:
        text = f"### Instruction:\n{example['instruction']}\n\n### Input:\n{example['input']}\n\n### Response:\n{example['output']}"
    else:
        text = f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"
    return {"text": text}

dataset = dataset.map(format_prompt)

def tokenize(examples):
    return tokenizer(
        examples["text"],
        truncation=True,
        max_length=512,
        padding=False,
    )

tokenized = dataset.map(tokenize, batched=True, remove_columns=dataset.column_names)

# Training arguments (note higher learning rate)
training_args = TrainingArguments(
    output_dir="./lora-output",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    num_train_epochs=1,
    learning_rate=2e-4,              # Higher than full fine-tuning
    bf16=True,
    logging_steps=10,
    save_steps=500,
    warmup_ratio=0.03,
    gradient_checkpointing=True,
    optim="adamw_torch_fused",
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized,
    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
)

trainer.train()

With SFTTrainer (TRL)

from trl import SFTTrainer, SFTConfig

sft_config = SFTConfig(
    output_dir="./sft-lora",
    max_length=1024,
    dataset_text_f