英文:
LMM Fine Tuning - Supervised Fine Tuning Trainer (SFTTrainer) vs transformers Trainer
问题
何时应选择监督微调训练器（SFTTrainer）而不是常规的Transformers训练器，当涉及到对语言模型（LLMs）进行指令微调时？根据我的了解，常规的Transformers训练器通常指的是无监督微调，通常用于在进行监督微调后执行输入输出模式格式化等任务。似乎有各种各样的微调任务具有类似的特性，但有些使用SFTTrainer，而其他使用常规训练器。在选择这两种方法之间应考虑哪些因素？
我正在寻找使用huggingface和trl库微调LLM以生成json到json转换（匹配json中的文本）。
英文:
When should one opt for the Supervised Fine Tuning Trainer (SFTTrainer) instead of the regular Transformers Trainer when it comes to instruction fine-tuning for Language Models (LLMs)? From what I gather, the regular Transformers Trainer typically refers to unsupervised fine-tuning, often utilized for tasks such as Input-Output schema formatting after conducting supervised fine-tuning. There seem to be various examples of fine-tuning tasks with similar characteristics, but with some employing the SFTTrainer and others using the regular Trainer. Which factors should be considered in choosing between the two approaches?
I looking for Fine Tuning a LLM for generating json to json transformation (matching texts in json) using huggingface and trl libraries.
答案1
得分: 0
same as Trainer but accepts a peft config so it can run lora fine-tuning.
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