Domain Adaptability Enhancement Full Process
Achieving performance breakthroughs in specialized areas requires synergistic optimization of data engineering and training strategies:
- Data preparation phase: It is recommended that a minimum of 5,000 domain QA data be collected in the format provided by the program.
dirty_chinese_dpo.jsonThe question and answer should contain (1) the full context of the question and answer (2) domain-specific terminology (3) examples of typical errors - Training strategy selection::
- Basic capability building: supervised fine-tuning with full data first (SFT)
train_sft_dirty.py3-5 rounds of training - Fine calibration: preference alignment with ORPO algorithm using the
RL_FineTuning/train_orpo.pyscripts, injecting domain expert labeled superiority samples on the
- Basic capability building: supervised fine-tuning with full data first (SFT)
- Validation Methods: Project reasoning scripts support batch test mode (
--mode batch), it is recommended to prepare 200 validation sets through automated evaluation
Special note: Overlaying knowledge retrieval modules is recommended for high-risk areas such as medical/legal to avoid purely generative risks.
This answer comes from the articleQwen3-FineTuning-Playground: a ready-to-use code base for fine-tuning Qwen3's big models.The
May not be reproduced without permission:AI productivity tools " How to improve the response accuracy of Qwen3 fine-tuned models in specific domains?
