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Fine-Tuning LLMs: A Complete Guide

March 06, 2026 • 7 views
Fine-tuning LLM LoRA QLoRA Tutorial

Introduction

Fine-tuning allows you to customize pre-trained LLMs for your specific use case. This guide covers the complete process from data preparation to deployment.

When to Fine-Tune

  • Domain-specific knowledge required
  • Consistent output format needed
  • Prompt engineering insufficient
  • Critical for production performance

Methods Comparison

Method VRAM Needed Training Time Quality
Full Fine-tuning High Slow Best
LoRA Low Fast Good
QLoRA Very Low Fast Good
Prefix Tuning Low Fast Moderate

Setup Environment

pip install torch transformers peft bitsandbytes trl

Prepare Your Dataset

# Format your data as instruction pairs
dataset = [
    {"instruction": "Classify sentiment", 
     "input": "This product is amazing!", 
     "output": "Positive"},
    # ... more examples
]

QLoRA Fine-Tuning Example

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

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    load_in_4bit=True
)

lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05
)

model = get_peft_model(model, lora_config)

Training Tips

  • Start with small learning rate (2e-4)
  • Use gradient checkpointing to save memory
  • Monitor validation loss for overfitting
  • Save checkpoints frequently

Resources

Source: JackAI Hub