We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| save_image_to_local | Not yet run | this paper's copy was not recorded; identical code first harvested from PKU-YuanGroup/MoE-LLaVA pointer only · get_code("e2e5c3a95a6aebb4") |
| save_video_to_local | Not yet run | this paper's copy was not recorded; identical code first harvested from PKU-YuanGroup/MoE-LLaVA pointer only · get_code("22930d58d32e2d08") |
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Recent advances demonstrate that scaling Large Vision-Language Models (LVLMs) effectively improves downstream task performances. However, existing scaling methods enable all model parameters to be active for each token in the calculation, which brings massive training and inferring costs. In this work, we propose a simple yet effective training strategy MoE-Tuning for LVLMs. This strategy innovatively addresses the common issue of performance degradation in multi-modal sparsity learning, consequently constructing a sparse model with an outrageous number of parameters but a constant computational cost. Furthermore, we present the MoE-LLaVA, a MoE-based sparse LVLM architecture, which uniquely activates only the top-k experts through routers during deployment, keeping the remaining experts inactive. Extensive experiments show the significant performance of MoE-LLaVA in a variety of visual understanding and object hallucination benchmarks. Remarkably, with only approximately 3B sparsely activated parameters, MoE-LLaVA demonstrates performance comparable to the LLaVA-1.5-7B on various visual understanding datasets and even surpasses the LLaVA-1.5-13B in object hallucination benchmark. Through MoE-LLaVA, we aim to establish a baseline for sparse LVLMs and provide valuable insights for future research in developing more efficient and effective multi-modal learning systems. Code is released at https://github.com/PKU-YuanGroup/MoE-LLaVA.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2401.15947")
get_code_for_paper("2401.15947")
have("2401.15947")
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