arXiv (OAI Expanded)
open access
Breaking the 1.58-bit Barrier for Ternary LLMs
Ternary Large Language Models (LLM) store every weight as one of three symbols $\{-1,0,+1\}$, so the cost of a ternary model is conventionally referenced to the information-theoretic $\log_2 3 \approx 1.585$ bits per weight. The prevailing deployment format packs five ternary weights into one byte (
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
- Baseline Shape Decides the Verdict: A Controlled Re-Examination of Ternary Language Models at 60K Parameters#1077326
- Many-Tier Instruction Hierarchy in LLM Agents#972429
- Objective Coefficient Rounding and Almost Symmetries in Binary Programs#984101
- Uso de modelos de linguagem de grande escala (LLMs) como suporte à modelagem de processos de negócio em BPMN#483762
- MTAC-IFBench: Benchmarking Instruction-Following in Multi-Turn Agentic Coding#1084892
Record · ID 1110702
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