LoRA Rank, Alpha, and Target Modules: Stop Using the Default

Most LoRA fine-tunes ship with rank eight, alpha sixteen, and attention-only targets because that is the default in the first tutorial the engineer found. This episode works through the empirical tuning path: the rank diminishing-returns curve, the alpha-equals-two-r heuristic and when it fails, the all-linear-layers strategy, and the diagnostic run pattern for finding the right configuration before committing to a full training run. Covers rsLoRA from arXiv two-three-one-two-dot-zero-three-seven-three-two. Watch the next video in this series on dataset curation. ▶ Watch next: Dataset Curation for LoRA: Shape, Size, and Legal Exposure https://www.youtube.com/watch?v=e3lI_fIAHGw Chapters: 0:00 Why Defaults Fail 2:11 The Rank r: Diminishing Returns 4:12 Alpha and the Scaling Identity 6:13 Target Module Selection 8:22 Dropout, Bias, and rsLoRA 10:32 The Diagnostic Run Pattern 12:27 Quiz Time #LLM #LoRA #AI --- Disclosure The avatars and voices in this video are AI-generated. All content -- research, scripts, lesson design, and the custom video engine -- is created by a CISSP, CISM, and PMP certified professional with a Master's in Project Management, a B.S. in Information Technology, and a Doctorate in Business Administration in progress. This channel exists to make learning accessible and straightforward. This channel is not affiliated with, endorsed by, or sponsored by Meta AI, OpenAI, Anthropic, Google DeepMind, Mistral, Alibaba (Qwen), Hugging Face, NVIDIA, AMD, Apple, Unsloth, Axolotl, vLLM, llama.cpp, or any model-host or fine-tuning-tooling vendor. All model-architecture, parameter-efficient fine-tuning (PEFT) method, hyperparameter, dataset-curation, evaluation, and deployment content is sourced from the upstream Hugging Face Transformers and PEFT documentation, the published model cards and licenses for Llama, Qwen, Mistral, Phi, Gemma, and other open-weight families, the original LoRA and QLoRA papers (Hu et al. 2021, Dettmers et al. 2023) and follow-up research, named-outlet reporting, and benchmark suites including MMLU, HumanEval, GSM8K, and MT-Bench, and is provided for educational purposes only. Open-weight model licenses vary substantially — some restrict commercial use, training on outputs, deployment in named jurisdictions, or use above named monthly-active-user thresholds. Always read the actual license file shipped with any model before deploying it for any non-personal use. Fine-tuning datasets may contain copyrighted, personally identifiable, or harmful content; you are responsible for the legal status of every example you train on. Hugging Face hub: huggingface.co | LoRA paper: arxiv.org/abs/2106.09685 | QLoRA paper: arxiv.org/abs/2305.14314 | Llama license terms: llama.com/llama-downloads/.