Unverified50% confidenceFactExact time
For video generation models with billions of parameters, LoRA fine-tuning typically requires adjusting less than 1% of the parameters.
1
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50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
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VerifiedFor a 7-billion-parameter model, LoRA typically requires training less than 1% of the total parameters, and GPU memory requirements can be reduced by more than 60%.76% similarVerifiedWhen fine-tuning with LoRA, the actual number of parameters that need to be trained is only 0.1%-1% of the original.68% similarUnverifiedLoRA微调只需训练不到1%的参数即可达到接近全参微调的效果，显存需求大幅降低至单张消费级显卡可承受范围68% similarUnverifiedMultimodal large models have extremely low factual recall accuracy in video question answering, which is difficult to improve even with supervised fine-tuning.68% similarUnverifiedFor a configuration of 1.6 trillion total parameters and 49 billion activated parameters, only about 3% of parameters participate in computation during actual inference.68% similar
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