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See how models, datasets, papers and organizations connect. Every link comes from public metadata: papers named on a model card, datasets and base models named in its tags, and the organization that published it. Click any dot to re-center; the smaller outer ring is two steps away.
SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization
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Connections
- ewin-reg/WeMM-Embedding-2B-GGUF Modelcited by
- H-Scale: Hessian-Guided Scale Refinement for NVFP4 Sub-Byte LLM Inference Papertwo steps away
- tencent/WeMM-Embedding-2B Modeltwo steps away
- The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally Papertwo steps away
- Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM Papertwo steps away
- Transforms for LLM Quantization: The Great Inversion and Format Co-Design Papertwo steps away
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