The PEFT Benchmark
We ran a massive internal benchmark across three of our proprietary datasets to definitively compare Parameter-Efficient Fine-Tuning (PEFT) methods: LoRA, Prompt Tuning, and traditional Bottleneck Adapters.
Findings
Prompt Tuning is incredibly parameter-efficient (only tuning a few virtual tokens), but we found it highly unstable to train. It requires massive amounts of data to converge properly and struggles with complex reasoning tasks.
Traditional Adapters (inserting small feed-forward layers between transformer blocks) are reliable but introduce inference latency because they add sequential operations to the forward pass.
LoRA (Low-Rank Adaptation) is still the absolute king. By injecting trainable low-rank decomposition matrices into the attention layers, it achieves performance nearly identical to full fine-tuning.
from peft import PeftModel
# The real magic of LoRA: Zero Inference Overhead
# Once trained, you merge the adapter weights directly into the base model
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3-8B")
peft_model = PeftModel.from_pretrained(base_model, "./lora-adapter")
# Merge and unload! Now inference is exactly as fast as the base model.
merged_model = peft_model.merge_and_unload()
merged_model.save_pretrained("./production-model")Crucially, LoRA weights can be mathematically merged back into the base model weights before deployment, resulting in zero inference latency overhead.