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LittleBit
Trends
- 1Samsung researchers propose sub-1-bit LLM compression method●Sub-1-Bit LLM Compression via Latent Factorization
Samsung's AI lab has released LittleBit, a technique that compresses large language models to less than one bit per weight using latent factorization. The approach aims to make big models far cheaper to store and run, and it is drawing attention among machine learning researchers debating how far quantization can go before accuracy collapses.
Repos
- SamsungLabs/LittleBit Official implementation of LittleBit (NeurIPS 2025) and its follow-up LittleBit-2 (ICML 2026)