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Qwen3MoeForCausalLM. Thank you to intervitens for assistance with memory-efficient conversion scripts!transformers-based training repository.modeling_qwen3_shared_moe.py.@misc{qwen3technicalreport,
title={Qwen3 Technical Report},
author={Qwen Team},
year={2025},
eprint={2505.09388},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.09388},
}
@misc{tan2024scatteredmixtureofexpertsimplementation,
title={Scattered Mixture-of-Experts Implementation},
author={Shawn Tan and Yikang Shen and Rameswar Panda and Aaron Courville},
year={2024},
eprint={2403.08245},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2403.08245},
}
@misc{hsu2025ligerkernelefficienttriton,
title={Liger Kernel: Efficient Triton Kernels for LLM Training},
author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
year={2025},
eprint={2410.10989},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.10989},
}
@misc{wijmans2025cutlosseslargevocabularylanguage,
title={Cut Your Losses in Large-Vocabulary Language Models},
author={Erik Wijmans and Brody Huval and Alexander Hertzberg and Vladlen Koltun and Philipp Krähenbühl},
year={2025},
eprint={2411.09009},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2411.09009},
}