A developer details a personal experiment fine-tuning the Qwen3.5 model on an RTX 3090, aiming to transform it into a candidate scorer for tasks like sentiment analysis or ticket routing. The experiment revealed that while accuracy improved, the model's mean negative log likelihood worsened, indicating a potential issue with the training data labels not aligning with the task's intent. This hands-on account emphasizes the importance of examining multiple metrics beyond simple accuracy and carefully scrutinizing training data for effective model development. AI
IMPACT Highlights the importance of detailed evaluation beyond accuracy in fine-tuning LLMs for specific tasks.
RANK_REASON Developer's personal learning experiment and retrospective on fine-tuning a specific model. [lever_c_demoted from research: ic=1 ai=1.0]
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