arXiv (All)
open access
Why Does Post-Training Quantization Work?
Post-training quantization compresses large language models (LLMs) by storing their weights at reduced precision, and each quantized weight introduces an error into the hidden states. Naively, these errors should accumulate with depth and corrupt next-token prediction; randomly initialized models ac
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
- Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation#1154971
- On the Recoverability of Private Information Unlearning in Large Language Models#799191
- On the Recoverability of Private Information Unlearning in Large Language Models#797768
- Debias-SparseGPT: Bias-Aware Pruning for Large Language Models#924373
- Debias-SparseGPT: Bias-Aware Pruning for Large Language Models#920142
Record · ID 999407
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