val_bpb
1.1194
Architecture
Maestro 1+7+1 Transformer
Optimizer
Parallel Muon
Artifact Size
~15.95 MB
Training Techniques
Quantization
GPTQ-lite
bits: 6
scope: null
Architecture
1+7+1 layer stack
Specialized layer stack with reasoning, completion, and validation layers including SolarShield gating and LeakyReLU(0.5)^2 activation
parameters: {"reasoning_layer":1,"completion_blocks":7,"validation_layer":1,"BigramHash_vocab_size":1536,"RoPE_dims":16}
SolarShield gating
Reality-locked gating mechanism balancing residual stream flow at layers L0 and L4
parameters: null
LeakyReLU(0.5)^2 activation
Replaces standard relu² or SiLU to maintain gradient flow with non-negative inductive bias
parameters: {"negative_slope":0.5}
Optimizer
Parallel Muon
weight_decay: 0.04
momentum: null
other_params: {"post_backward_reduce_scatter":true,"local_NS5":true,"all_gather":true}
Weight Averaging
EMA + Tight SWA
parameters: {"EMA_decay":0.997,"SWA_every":50}
Evaluation
sliding window eval
parameters: {"mode":"torch.inference_mode()","stateless":true}
Test-Time Training
score-first TTT
parameters: {"learning_rate":0.002,"epochs":3,"momentum":0.9,"freeze_blocks":0,"chunk_tokens":32768,"batch_seqs":32,"grad_clip":1}
Regularization
weight decay
parameters: {"value":0.04}
Novel Contributions
- Maestro OS framework with 1+7+1 layer architecture for reasoning, completion, and validation
- SolarShield gating mechanism for dynamic residual stream balancing
- Use of LeakyReLU(0.5)^2 activation to maintain gradient flow with non-negative inductive bias
- Integration of Parameter Banking and Parallel Muon optimizer
- Legal TTT protocol with score-first test-time training on previously scored chunks
- Combination of EMA and Tight SWA weight averaging
- GPTQ-lite int6 quantization with LZMA compression