Updated on 2025-11-04 GMT+08:00
FAQs Related to LLM Fine-Tuning and Training
- How Do I Enable Models to Learn Unsupervised Domain-Specific Knowledge If the Data Volume Is Insufficient for Incremental Pre-training?
- How Do I Adjust Training Parameters to Maximize the Pangu Model Performance?
- How Do I Determine Whether the Pangu Model Training Status Is Normal?
- How Do I Evaluate Whether the Fine-Tuned Pangu Model Is Normal?
- How Do I Adjust Inference Parameters to Maximize the Pangu Model Performance?
- Why Does the Fine-Tuned Pangu Model Always Repeat the Same Answer?
- Why Does the Fine-Tuned Pangu Model Generate Garbled Characters?
- Why Is the Answer of the Fine-Tuned Pangu Model Truncated Abnormally?
- Why Can the Fine-Tuned Pangu Model Only Answer the Questions in the Training Sample?
- Why Does the Fine-Tuned Pangu Model Return Different Answers to the Same Question in the Training Sample?
- Why Is the Performance of the Fine-Tuned Pangu Model in Actual Scenarios Worse Than That During Evaluation?
- Why Is the Performance of the Fine-Tuned Pangu Model Unsatisfactory in Multi-Turn Dialogues?
- Why Is the Performance of the Fine-Tuned Pangu Model Unsatisfactory When the Data Volume Is Sufficient?
- Why Is the Performance of the Fine-Tuned Pangu Model Unsatisfactory Even Though Both the Data Volume and Quality Meet Requirements?
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