LLM Fine-Tuning Stack (PyTorch, HuggingFace, PEFT/LoRA, W&B, vLLM) overview

A specialized stack for adapting large language models to domain-specific tasks through parameter-efficient fine-tuning. PyTorch provides the training runtime. HuggingFace Transformers supplies pre-trained model weights, tokenizers, and the Trainer API. PEFT (Parameter-Efficient Fine-Tuning) with LoRA adapters enables fine-tuning billion-parameter models on consumer or single-node GPU hardware by training only a small fraction of weights. Weights & Biases (W&B) tracks training runs, hyperparameters, loss curves, and evaluation metrics. vLLM provides high-throughput inference with PagedAttention for deploying the fine-tuned model. Python is the sole language across the pipeline. The key tradeoff is that LoRA adapters trade some quality ceiling for dramatically lower compute cost; full fine-tuning on large models still requires multi-GPU clusters.