Fine-Tuning LLMs: Quick Reference for LoRA, QLoRA, and More

Struggling to pick between LoRA, QLoRA, and full fine-tuning for your next LLM project? This reference distills the critical differences, code idioms, hardware requirements, and decision factors into one scannable resource. Use it to save compute, budget, and time on your next model adaptation—whether you’re handling medical Q&A, legal summarization, or customer chatbots.

Key Takeaways:

• Rapidly compare LoRA, QLoRA, and full fine-tuning for LLMs using a single table.
• Use a decision tree to select the right method for your compute, data, and deployment needs.
• Copy-paste code templates for Hugging Face Transformers and PEFT workflows.
• Reference key hyperparameters, memory needs, and best practices for each approach.
• Find pro tips and common mistakes in one place—no re-reading full guides required.

Quick Overview Table

This table summarizes the essential differences between LoRA, QLoRA, and full fine-tuning. If you need deeper background, refer to our deep dive on fine-tuning LLMs.

Reference: LLM Fine-Tuning: LoRA to QLoRA Production Strategies and How to Fine-Tune LLMs with LoRA and QLoRA

4. Practical Considerations for Fine-Tuning

When deciding between LoRA, QLoRA, and full fine-tuning, consider the specific requirements of your project. For instance, if you are working with a limited dataset, LoRA or QLoRA may provide the best balance between performance and resource usage. Additionally, think about the deployment environment—if you’re targeting edge devices, QLoRA’s lower memory footprint can be a game-changer.

5. Future Trends in Fine-Tuning Techniques

As the field of NLP evolves, new methods for fine-tuning LLMs are emerging. Techniques such as adapter tuning and prompt tuning are gaining traction, offering alternatives that may be more efficient than traditional methods. Keeping an eye on these trends can help you choose the best approach for future projects.

LLM Fine-Tuning Decision Tree

Don’t waste cycles on the wrong approach. Use this streamlined decision tree to choose the optimal fine-tuning method for your project:

• Do you require all model weights to be updated (e.g., architecture change, new modalities)?
  • Yes → Full Fine-Tuning
  • No → Continue
• Is your GPU memory >= 24GB? (A100, H100, 3090, etc.)
  • Yes → Full Fine-Tuning or LoRA
  • No → Continue
• Is your GPU memory 12-24GB? (A10, T4, 3060, cloud VMs)
  • Yes → LoRA
  • No → Continue
• Is your GPU memory 6-12GB? (consumer cards, laptops, cloud spot)
  • Yes → QLoRA
  • No → Consider Distillation or RAG

For pipeline-level integration and deployment patterns, see our ML pipelines reference.

Practical Code Snippets

Jump straight to working code for each fine-tuning approach. These templates are ready for adaptation to your Hugging Face and PEFT-based projects.

1. Full Fine-Tuning (Transformers + Trainer)

from transformers import AutoModelForCausalLM, Trainer, TrainingArguments

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
args = TrainingArguments(
    per_device_train_batch_size=2,
    gradient_accumulation_steps=16,
    fp16=True,
    output_dir="./finetuned",
    num_train_epochs=2,
)
trainer = Trainer(
    model=model,
    train_dataset=your_train_dataset,
    args=args,
)
trainer.train()  # Updates ALL weights, requires large VRAM

2. LoRA (Hugging Face PEFT)

from transformers import AutoModelForCausalLM, Trainer, TrainingArguments
from peft import LoraConfig, get_peft_model

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
lora_config = LoraConfig(
    r=8,                # Rank of LoRA matrices
    lora_alpha=16,
    lora_dropout=0.05,
    target_modules=["q_proj", "v_proj"],
    bias="none",
    task_type="CAUSAL_LM",
)
model = get_peft_model(base_model, lora_config)

args = TrainingArguments(
    per_device_train_batch_size=2,
    gradient_accumulation_steps=16,
    fp16=True,
    output_dir="./lora-finetuned",
    num_train_epochs=2,
)
trainer = Trainer(
    model=model,
    train_dataset=your_train_dataset,
    args=args,
)
trainer.train()  # Only LoRA adapters trained; much lower VRAM

3. QLoRA (PEFT + bitsandbytes 4-bit quantization)

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

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,         # Enable 4-bit quantization
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16"
)
base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=bnb_config,
    device_map="auto"
)
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    lora_dropout=0.05,
    target_modules=["q_proj", "v_proj"],
    bias="none",
    task_type="CAUSAL_LM",
)
model = get_peft_model(base_model, lora_config)

# Trainer code as above

For full, step-by-step pipelines with deployment and troubleshooting, see our complete LLM fine-tuning guide.

Hyperparameters & Resource Cheat Sheet

• LoRA/QLoRA: Increasing r improves capacity but increases VRAM (r=8-16 is common for Llama3).
• QLoRA: 4-bit quantization may slightly reduce accuracy; ideal for prototyping or cost-sensitive scenarios.
• For efficient pipelines, see our ML pipeline best practices.

Real-World Use Cases & Recommendations

For production deployment and cost optimization, see this LLM fine-tuning strategy guide.

Pitfalls, Tips & Quick Fixes

• Forgetting to Freeze Backbone:
  • LoRA/QLoRA only train adapter weights. If you accidentally unfreeze the full model, you lose all efficiency gains.
• VRAM Exhaustion:
  • Full fine-tuning on consumer GPUs almost always fails with OOM errors. Double-check per_device_train_batch_size and use gradient checkpointing if you must proceed.
• Underpowered Quantization:
  • QLoRA’s 4-bit models can degrade performance on rare tasks or when adapters are too small (r<4).
• Wrong Target Modules:
  • Adapters must match the model’s architecture. “q_proj” and “v_proj” cover most Llama/OPT/ChatGLM, but verify with model.named_modules().
• Deployment Compatibility:
  • LoRA/QLoRA adapters require loading logic in serving pipelines. Merge adapters for pure ONNX/TensorRT export.
• Hyperparameter Myths:
  • LoRA can use higher learning rates and batch sizes than full fine-tuning, but overfitting is still possible on very small datasets.

For a complete walkthrough and more detailed pitfalls, review our comprehensive LLM fine-tuning guide and check out external resources on LoRA/QLoRA best practices.

Pro Tips:

• Use bitsandbytes for QLoRA to avoid custom CUDA builds.
• For reproducibility, set torch.manual_seed before all model and data operations.
• For code generation and pipeline automation, see AI code generation techniques.

Conclusion & Next Steps

Conclusion & Next Steps

Bookmark this cheat sheet for quick reference the next time you fine-tune a language model. For stepwise tutorials, pitfalls, and production deployment, consult our LLM fine-tuning guide. For optimizing the end-to-end ML workflow, review our ML pipelines best practices. Stay up to date by revisiting this page as new fine-tuning techniques emerge and hardware landscapes evolve.

For stepwise tutorials, pitfalls, and production deployment, consult our LLM fine-tuning guide. For optimizing the end-to-end ML workflow, review our ML pipelines best practices. Stay up to date by revisiting this page as new fine-tuning techniques emerge and hardware landscapes evolve.