Fine-tuning Qwen2.5 with LoRA yields structured, not necessarily correct, outputs

This article explores the process of fine-tuning the Qwen2.5 model using the LoRA technique. It demonstrates that while fine-tuning can lead to more structured outputs, this does not necessarily equate to improved reasoning capabilities. The author provides a practical walkthrough of Supervised Fine-Tuning (SFT) to illustrate this point. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Demonstrates that fine-tuning can improve output structure without enhancing core reasoning, impacting how model improvements are evaluated.

RANK_REASON The cluster describes a technical paper detailing a method for fine-tuning an existing model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Medium — fine-tuning tag →

• LoRA
• Qwen2.5

• paper
• model release