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FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The recent introduction of FLoRA, a method for fine-tuning large language models (LLMs), marks a significant advancement in the field of artificial intelligence. As LLMs continue to grow in complexity, the need for efficient training techniques becomes crucial. FLoRA utilizes fused forward-backward adapters to enhance parameter efficiency and reduce inference-time latencies, making it easier for developers to implement these powerful models in real-world applications. This innovation not only streamlines the training process but also opens up new possibilities for utilizing LLMs in various industries.
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