Adapting large language models by training only a low-rank decomposition BA added to frozen weight matrices matches full fine-tuning while cutting trainable parameters by orders of magnitude and adding no inference latency.
Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models
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A literature survey that introduces a taxonomy for computational and communication efficiency in federated learning with foundation models and discusses PEFT, framework readiness, and open research questions.
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LoRA: Low-Rank Adaptation of Large Language Models
Adapting large language models by training only a low-rank decomposition BA added to frozen weight matrices matches full fine-tuning while cutting trainable parameters by orders of magnitude and adding no inference latency.