Optimization Qlora

QLoRA Fine-Tuning

🎯 Trigger Conditions

Use when asked about QLoRA (Quantized Low-Rank Adaptation), memory-efficient fine-tuning, or parameter-efficient tuning for DSPy.

📚 Prerequisites

• peft package installed
• bitsandbytes package installed
• GPU with sufficient VRAM
• Training data prepared

🛠️ QLoRA Implementation

1. Basic QLoRA Setup

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

# Configure quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True
)

# Load model with quantization
model = AutoModelForCausalLM.from_pretrained(
    "your-model",
    quantization_config=bnb_config,
    device_map="auto"
)

# Prepare for training
model = prepare_model_for_kbit_training(model)

# Apply LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none"
)

model = get_peft_model(model, lora_config)

2. QLoRA for DSPy

import dspy
from peft import LoraConfig, get_peft_model

# Load DSPy program
program = dspy.ChainOfThought("question -> reasoning -> answer")

# Apply QLoRA to underlying model
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["query", "value"],
    lora_dropout=0.1
)

program.model = get_peft_model(program.model, lora_config)

3. QLoRA Training

from transformers import TrainingArguments, Trainer

# Configure training
training_args = TrainingArguments(
    output_dir="qlora-output",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    fp16=True,
    logging_steps=10
)

# Create trainer
trainer = Trainer(
    model=program.model,
    args=training_args,
    train_dataset=train_dataset,
    tokenizer=tokenizer
)

# Train
trainer.train()

4. QLoRA Configuration Options

lora_config = LoraConfig(
    # Rank
    r=8,  # Low-rank dimension
    
    # Alpha
    lora_alpha=16,  # Scaling factor
    
    # Target modules
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    
    # Dropout
    lora_dropout=0.05,
    
    # Bias
    bias="none",
    
    # Task type
    task_type="CAUSAL_LM"
)

⚠️ Pitfalls

• VRAM requirements: Still requires significant VRAM
• Quality trade-off: Quantization affects quality
• Training stability: QLoRA can be unstable
• Compatibility: Not all models support QLoRA

📖 References

• QLoRA Paper
• PEFT QLoRA