Symptom

The goal of accuracy tuning during LLM inference is to ensure the model's inference capability on the Ascend platform. Typically, this is done by evaluating the model's inference ability through dataset calibration or by comparing its output with that of a benchmark model. The following issues are commonly seen during accuracy tuning:

• The model is babbling and unable to have a normal conversation.
• The model and the benchmark exhibit semantic deviations in their responses, or there are significant differences in the answers to deterministic questions (for example, results of true/false questions).
• The dataset evaluation fails.

Although the symptoms and root causes of common issues vary, they can all be quickly diagnosed and analyzed using the LLM inference accuracy issue analysis methods introduced in this article.