Unverified50% confidenceFactExact time
In the context of fine-tuning, "sequence dataset" usually refers to converting raw corpus into input-output pairs that the model can consume, with the format determined by the training objective: instruction fine-tuning requires constructing instruction-response pairs, distillation requires the teacher model's output as labels, and embedding model fine-tuning requires positive and negative sample pairs.
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Relevance
10/28/2026
First Seen
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AI Large Model Fine-Tuning Full Process in Practice: Thirteen Lessons from Data to Deployment
bilibili前端vue实战项目10/28/2026
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VerifiedInstruction fine-tuning uses a dataset in question-answer pair format for training, enabling the model to better follow natural language instructions74% similarUnverifiedDuring the training phase, the decoder can use teacher forcing to process the entire target sequence in parallel, whereas during inference the model can only generate output autoregressively one token at a time.72% similarUnverifiedProcedurally generated data refers to training samples automatically synthesized through algorithms and rules, rather than manually annotated real data.72% similarUnverified合成数据通常由一个强大的教师模型生成问答对、推理链或代码样本，再用于训练学生模型70% similarUnverifiedThe course emphasizes that data engineering is an indispensable part of fine-tuning, describing it as "how to turn this data into valuable sequence datasets".70% similar
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