Papers › MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter

MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter

7 Jun 2024arXiv:2406.04984archive 2025-07-28

Jitai Hao, Weiwei Sun, Xin Xin, Qi Meng, Zhumin Chen, Pengjie Ren, Zhaochun Ren

Parameter-Efficient Fine-tuning (PEFT) facilitates the fine-tuning of Large Language Models (LLMs) under limited resources. However, the fine-tuning performance with PEFT on complex, knowledge-intensive tasks is limited due to the constrained model capacity, which originates from the limited number of additional trainable parameters. To overcome this limitation, we introduce a novel mechanism that fine-tunes LLMs with adapters of larger size yet memory-efficient. This is achieved by leveraging the inherent activation sparsity in the Feed-Forward Networks (FFNs) of LLMs and utilizing the larger capacity of Central Processing Unit (CPU) memory compared to Graphics Processing Unit (GPU). We store and update the parameters of larger adapters on the CPU. Moreover, we employ a Mixture of Experts (MoE)-like architecture to mitigate unnecessary CPU computations and reduce the communication volume between the GPU and CPU. This is particularly beneficial over the limited bandwidth of PCI Express (PCIe). Our method can achieve fine-tuning results comparable to those obtained with larger memory capacities, even when operating under more limited resources such as a 24GB memory single GPU setup, with acceptable loss in training efficiency. Our codes are available at https://github.com/CURRENTF/MEFT.

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my_prepare_model_for_int8_training currentf/meft/mistral_finetune.py official repository ran no licence file found · pointer only · 8ac2d521295a4d00 · report
KVLlamaMLP CURRENTF/MEFT/modeling/my_llama.py official repository unverified no licence file found · pointer only · a862da4beb100ff4 · report
create_faiss_index currentf/meft/modeling/hnswlib_vector_database.py official repository unverified no licence file found · pointer only · 2fc4082861047a90 · report
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get_IVFxPQy currentf/meft/modeling/hnswlib_vector_database.py official repository unverified no licence file found · pointer only · 9e0c2a8bd5d9a627 · report
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Mixture-of-Expertsparameter-efficient fine-tuning

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