1. Why Fine-Tune Open-Weights Models?
While commercial LLM APIs offer general knowledge, fine-tuning open-weights models like Llama 3 yields superior accuracy on niche industry tasks at a fraction of continuous API costs.
2. Low-Rank Adaptation (LoRA)
LoRA freezes core base model parameters and injects trainable rank decomposition matrices, reducing GPU memory footprint requirements by up to 80%.
from peft import LoraConfig, get_peft_model
peft_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none"
)3. Production Deployment with vLLM
Deploy fine-tuned weights using high-throughput serving frameworks like vLLM for continuous low-latency inference.
