Why Fine-tune Large Language Models?

While models like GPT-4 and Llama show impressive zero-shot capabilities, fine-tuning on domain-specific data can significantly boost performance for specialized tasks. The challenge is doing this efficiently without the massive computational cost of full model retraining.

Parameter-Efficient Fine-Tuning with LoRA

Python 
from transformers import AutoModelForCausalLM
from peft import LoraConfig, get_peft_model

# Load base model
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    load_in_8bit=True,
    device_map="auto"
)

# Configure LoRA
lora_config = LoraConfig(
    r=8,                    # Rank
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

# Apply LoRA
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
# trainable: 0.06% of total parameters

Training Configuration

Python 
from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    fp16=True,
    logging_steps=10,
    save_strategy="epoch",
    warmup_ratio=0.03,
    lr_scheduler_type="cosine"
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset
)

trainer.train()

Best Practices

💡 Key Tips

  • Quality over quantity: 1K quality examples beat 10K noisy ones
  • Consistent formatting: Use same prompt template throughout
  • Start small: Test on 7B before scaling to 70B
  • Monitor overfitting: Validate regularly on held-out data
  • Learning rate: Use 1e-4 to 3e-4 for LoRA

Conclusion

PEFT techniques like LoRA make LLM fine-tuning accessible on consumer hardware. Focus on data quality, appropriate hyperparameters, and rigorous evaluation for best results.