A recent benchmark on an L40S GPU compared three fine-tuning methods for the Qwen2.5-1.5B-Instruct model: full fine-tuning, LoRA, and QLoRA. Full fine-tuning required the most memory, consuming 36.9 GiB with a micro-batch size of 4. LoRA was faster and used less memory than full fine-tuning, at 14.5 GiB, and achieved a slightly lower held-out loss. Surprisingly, QLoRA was slower than both other methods and used more memory than LoRA. AI
IMPACT Provides practical guidance on choosing fine-tuning methods based on memory and speed constraints for smaller models.
RANK_REASON Benchmark results comparing different fine-tuning techniques for a specific model. [lever_c_demoted from research: ic=1 ai=1.0]
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
