arXiv (All)
open access
All for 1-Bit: Towards Genuine 1-Bit Post-Training Quantization for LLMs
Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deployment. Weight binarization is a promising solution, but existing binarization-based post-training quantization (PTQ) methods usually far exceed the nomin
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
- Why Does Post-Training Quantization Work?#673514
- KBBQ: A Predictive Noise Law and the Limits of Spectrum Flattening in FP4 Quantization#985520
- Q-SPT: Learnable Query-Based Compression for Low-Frame-Rate Speech Tokenization#1156764
- Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool#1014774
- Anonymization, Not Elimination: Utility-Preserved Speech Anonymization#925137
Record · ID 973763
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