SAGE: Pushing 2-Bit Vector-Quantized LLMs toward Real-World Usability
cs.AI
Submitted: 2026-10-10
Updated: 2026-10-10
Code: https://github.com/Zincwolf/sage
Sources
- OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs
- Training Verifiers to Solve Math Word Problems
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
- GLM-5: from Vibe Coding to Agentic Engineering
- Kimi K3: Open Frontier Intelligence
- Ministral 3
- Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization
- MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization
- Qwen3 Technical Report
- PCDVQ: Enhancing Vector Quantization for Large Language Models via Polar Coordinate Decoupling
- Instruction-Following Evaluation for Large Language Models
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