Unverified50% confidenceFactExact time
Gemma 2B微调通常采用LoRA（低秩适应）等参数高效微调技术，只训练原始权重的极小部分（通常不到1%）
1
Sources
50%
Confidence
Long-term
Relevance
7/23/2026
First Seen
Sources
通义千问Qwen 3.8 Max实测：开源模型如何逼近顶级闭源水准
Related Claims
UnverifiedFine-tuning typically requires only one percent or less of the original training cost to achieve domain performance close to that of a dedicated model.71% similarUnverified指令微调不需要从头训练，通常仅需基础训练计算量的1%以下即可显著改变模型行为模式69% similarUnverified通过LoRA等轻量化微调技术，可以用少量特定数据对模型进行定向训练，生成特定类型内容69% similarVerifiedParameter-efficient fine-tuning (PEFT) methods such as LoRA and Adapter can compress the number of trainable parameters to less than 1% of the total parameters.69% similar
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