- 更新:2023-08-24 13:56:53
- 首发:2023-08-23 23:21:29
- 大模型
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The article primarily discusses the types and quality of data required for Supervised Fine-Tuning (SFT). It covers the following aspects:
- Objectives of Supervised Fine-Tuning : Enhancing performance in specific tasks, domain adaptability, and the interpretability and controllability of the model, with an overarching goal to boost system robustness.
- Core Considerations : These include the diversity of data, avoiding treating SFT merely as data supplementation, appropriately incorporating few-shot learning and COT data, emphasizing data quality over quantity in SFT, and recognizing that increasing data volume without diversity brings diminished returns.
- Data Quality Requirements : These considerations touch on the length restrictions for questions and answers, the accuracy of answers, the selection of data based on industry requirements, the diversity of necessary NLP abilities, and the caution against too much vertical domain data.
- Specific Examples : The article provides both good and poor dataset examples to illustrate how to choose and evaluate data.
- Q&A Section : This part explains why including the ability to write code in SFT is essential, emphasizing its importance in improving reasoning and structured output abilities.
In summary, the article offers comprehensive guidance on how to conduct supervised fine-tuning, underlining the importance of data diversity and quality, and presents implementation strategies and examples to support these points.
由于我使用的方案并不需要“快捷指令”等APP的配合。也无需任何系统权限。因此存在被滥用可能,请大家不要因为此事联系我,谢谢。
直接问AI吧😂
作者老哥,代码不开源。可以大致说一下实现思路吗😕
谢谢,你写的最详细,也很有效的解决了撕裂问题
很棒的教程,比我之前配置ap的方式更优雅