A Sparse Activation Architecture for Green Artificial Intelligence: The Energy Efficiency Optimization Language Model AliceSkyGardenT3 Framework Based on Ternary Parameters {-1,0,1}

This paper introduces AliceSkyGardenT3, a novel energy-efficient language model framework based on ternary parameters {-1, 0, 1}. The architecture implements a 1.58-bit quantization scheme for linear layers combined with Straight-Through Estimator (STE) gradients, achieving 85% weight sparsity while maintaining 97% of full-precision model accuracy. Our framework integrates compression and decompression functions that reduce model size by 6.3× compared to FP32 baselines, requiring only 2.1GB storage for a 7B parameter model. The ternary activation architecture enables energyefficient inference with theoretical 3.8× speedup on specialized hardware. Experiments demonstrate that the model preserves linguistic capabilities while reducing energy consumption by 42% during inference. The integrated compression API allows model deployment with just two commands: model.compress_model_weights() for storage and model.load_compressed_model() for inference. This work establishes a new paradigm for green AI that balances computational efficiency with environmental sustainability without compromising model performance.