arXiv (All)
open access
Complete-muE: Optimal Hyperparameter Transfer and Scaling for MoE Models
We propose Complete-muE, a framework which targets hyperparameter transfer across dense FFN and any Mixture-of-Experts (MoE) setups in transformer blocks. Existing tools such as $μ$P (requires fixed architectue) or SDE (requires fixed per-step token count) cannot directly solve the hyperparameter tr
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving#998172
- Dynamic Expert Quantization for Scalable Mixture-of-Experts Inference#999691
- DecoMoE: Decoupling Visual Propagation and Expert Computation for Efficient Multimodal MoE Inference#1143424
- Mixture of experts architectures for machine learning interatomic potentials#614950
- Mixture of experts architectures for machine learning interatomic potentials#613983
Record · ID 1117434
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive