Title: Discussion 8 — Picking a Winner Across Five LoRA Runs
Course: AIINFRA 201: Model Adaptation — Fine-Tuning & Quantization > Section: Week 08 — Axolotl, Config-Driven Training, and Experiment Tracking

💬 Discussion 8 — Picking a Winner Across Five LoRA Runs (20 pts)

Prompt: Imagine you're running five different LoRA rank configurations (say, rank 4, 8, 16, 32, and 64) on the same base model and dataset, and you need to decide which one to ship. Running them one at a time and eyeballing the terminal output would work, but it's slow and error-prone. In 150+ words, explain how wiring up Weights & Biases experiment tracking (via wandb_project / wandb_entity in your Axolotl config) would help you make this decision faster and with more confidence than running the configs sequentially and comparing raw logs. Be specific: what would you actually look at in the W&B dashboard, and what would make you choose one rank over another? Respond to two peers: Reply to at least two classmates. Push on their reasoning a bit — for example, ask what they'd do if two runs had nearly identical validation loss curves but very different training times or GPU utilization, or whether they'd weigh overfitting signals (train/val loss divergence) differently than raw final loss.