GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets
cs.LG, cs.AI
Submitted: 2026-05-18
Updated: 2026-08-27
Code: https://github.com/open-compass/opencompass
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- The Llama 3 Herd of Models
- OstQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting
- Qwen Technical Report
- Categorical Reparameterization with Gumbel-Softmax
- BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- PB-LLM: Partially Binarized Large Language Models
- FlatQuant: Flatness Matters for LLM Quantization
- Extreme Compression of Large Language Models via Additive Quantization
- QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
- BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments
- Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search
- BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization
- HellaSwag: Can a Machine Really Finish Your Sentence?
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