I am designing a fine-tuning pipeline for a production-grade LLM and am operating under a strict R&D budget. To optimize costs, I am considering a Small-to-Large staging strategy.
The Workflow:
Stage 1 (Local/Validation): I am using a local RTX 5060 Ti (16GB VRAM) to fine-tune Qwen 2.5 8B. My goal here is to validate the data preprocessing pipeline, verify loss convergence, and perform initial hyperparameter sweeps (Rank, Alpha, Learning Rate).
Stage 2 (Cloud/Scaling): Once metrics (eval loss, Rouge-L, or task-specific benchmarks) stabilize, I plan to migrate the exact same codebase and LoRA configuration to a RunPod instance (NVIDIA L40S 48GB VRAM) to train Qwen 3 32B.
The Concerns:
While the codebase is portable, I am concerned about the hyperparameter transferability between architectures (Qwen 2.5 8B vs. Qwen 3 32B).
Is it industry standard to assume that an optimal LoRA r (rank) and alpha (alpha) on an 8B model will remain optimal (or even effective) on a 32B variant of a newer generation?
Scaling Laws: Does the learning rate generally require downward scaling when moving from 8B to 32B to prevent gradient instability, or is the relationship typically stable across these parameter counts?
Data Quality: Is an 8B model "sensitive" enough to catch subtle data quality issues (e.g., formatting inconsistencies) that would later plague the 32B model, or is there a risk of "false negatives" where the small model fails but the large model would have succeeded?
I want to ensure I'm not "over-fitting" my strategy to the limitations of my local hardware before moving to a paid cloud environment.