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Found 2 papers, 1 papers with code
RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
We investigate parameter-efficient fine-tuning (PEFT) methods that can provide good accuracy under limited computational and memory budgets in the context of large language models (LLMs).
Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression
To address this, we ask: can we quickly compress large generalist models into accurate and efficient specialists?
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