Fine-Tuning Strategy Selector

Full Fine-Tuning vs LoRA: Side by Side

Aspect	Full Fine-Tune	LoRA/PEFT
Trainable Parameters	All parameters (~100%)	~0.1-1% of parameters
GPU Memory	High (full model + optimizer states)	Lower (base frozen, small adapters)
Training Speed	Slower (more params to update)	Faster (fewer params)
Storage per Task	Full model copy per task	Small adapter weights (~10-50MB)
Task Switching	Load different models	Swap adapter weights
Performance Ceiling	Higher (full flexibility)	Near full fine-tune for most tasks
Risk of Catastrophic Forgetting	Higher	Lower (base weights frozen)
Best For	Maximum performance, large datasets	Limited compute, multi-task, iteration

                    
# LoRA Configuration Example

from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(

    r=8,                # Rank of update matrices

    lora_alpha=16,       # Scaling factor

    lora_dropout=0.05,   # Dropout for regularization

    bias="none",          # Don't train bias terms

    task_type="SEQ_CLS"  # Sequence classification

)

model = get_peft_model(model, lora_config)

model.print_trainable_parameters()

# Output: trainable params: 294,912 || all params: 66,955,010 || trainable%: 0.44

Real-World Decision Scenarios

Click on a scenario to see the reasoning behind the recommendation.

Capstone Project Demo LoRA

Student project with access to Google Colab free tier (T4 GPU). Need to fine-tune BERT for sentiment analysis on 10k examples.

Limited GPU memory on Colab. LoRA allows fine-tuning on free tier hardware.

Production Classifier Full Fine-Tune

Company deploying a spam classifier with 1M training examples. Have dedicated A100 cluster. Need maximum accuracy.

Ample compute + large dataset + maximum performance requirement = full fine-tune.

Multi-Language Adapters LoRA

Need to support 10 different languages with the same base model. Each language has ~5k labeled examples.

LoRA adapters can be swapped without reloading the base model. Storage-efficient multi-task setup.

Rapid Iteration LoRA

Research team experimenting with 50 different hyperparameter combinations. Need fast turnaround.

LoRA trains faster and uses less memory, enabling more experiments per day.

Domain-Specific LLM Full Fine-Tune

Legal tech startup adapting Llama 2 for contract analysis. 500k document corpus. Running on H100 cluster.

Deep domain adaptation benefits from updating all parameters. Sufficient compute available.

Consumer Device LoRA

Personalized model for mobile app. Users can customize on their phone. Need to run on 8GB device.

LoRA adapters are small enough to download and swap on-device. Base model stays frozen.