GRT-30M-QA (MatterWaveAI/grt-30m-qa)

GRT-30M-QA (GRTForQuestionAnswering) is a 100% Native Rotational Extractive Question Answering Model (29,306,020 parameters, zero nn.Linear layers, zero nn.Conv1d layers) built on MatterWaveAI/grt-30m.

GRTForQuestionAnswering replaces traditional dense span projection layers (nn.Linear(hidden_size, 2)) with a 2-channel GRTCosineClassificationHead (qa_outputs) parameterized by planar-pair phase_angles ([2, 128]) and softplus radial pair_scales ([2, 128]), emitting start_logits and end_logits across context token positions.

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8-Way Classical Geometric Optimizer Ablation (GRTForQuestionAnswering)

All 8 classical geometric optimizers in transformers.models.grt.optimization_grt were benchmarked under an identical 200-step adaptation protocol (seed = 42, batch_size = 24, backbone_lr = 1.8e-4) unfreezing the top rotational transformer block blocks[-1] (811,651 parameters), final_norm (128 parameters), and qa_outputs (523 parameters) for 812,430 total trainable parameters (0 nn.Linear):

Rank	Optimizer (optimization_grt.py)	Final Train Loss	Val Span Loss	Exact Match (EM %)	Span Token F1 (%)	Start Acc (%)	End Acc (%)	Wall Time (200 Steps)
1	polar_natural_adam (PolarNaturalAdam (Riemannian Polar Metric + Cauchy-Riemann Hessian))	0.1385	0.6512	75.00%	83.57%	83.75%	76.25%	219.08s
2	cyclide_adam (CyclideAdam (Dupin Cyclide Conic Metric + Deviatoric Twist))	0.1382	0.6520	75.00%	83.57%	83.75%	76.25%	225.19s
3	pair_adam (PairAdam (Factored Toroidal Baseline))	0.1383	0.6532	75.00%	83.57%	83.75%	76.25%	207.23s
4	symplectic_adam (SymplecticAdam (Gromov-Liouville Non-Squeezing Floor + Casimir Cavity))	0.1391	0.6537	75.00%	83.57%	83.75%	76.25%	261.38s
5	huygens_symplectic_adam (HuygensSymplecticAdam (Two-Stage Huygens -> Symplectic Curriculum))	0.1391	0.6537	75.00%	83.57%	83.75%	76.25%	146.70s
6	gyric_adam (GyricAdam (Poinsot-Lande Gyroscopic Projection + Thomas Precession))	0.1393	0.6541	75.00%	83.57%	83.75%	76.25%	221.44s
7	huygens_adam (HuygensAdam (Circular Order Parameter R_p Splay Torque))	0.1378	0.6557	75.00%	83.57%	83.75%	76.25%	152.14s
8	hessian_pair_adam (HessianPairAdam (Sophia-Torus Cauchy-Riemann Curvature))	0.1387	0.6506	73.75%	83.39%	83.75%	75.00%	232.96s
—	Step-0 Initialized Rotational Head (Before Adaptation)	—	8.9793	0.00%	0.45%	3.75%	0.00%	—

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Quickstart (transformers)

import torch
from transformers import AutoTokenizer, GRTForQuestionAnswering

model_id = "MatterWaveAI/grt-30m-qa"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = GRTForQuestionAnswering.from_pretrained(model_id)

prompt = (
    "Question: Who serves as the chief architect at MatterWave AI?\n"
    "Context: While Elena Rostova directs European Space Agency in Central Zurich, "
    "the chief architect at MatterWave AI leading Givens Rotational Transformer is David Householter."
)
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model(**inputs)
s = int(outputs.start_logits.argmax(dim=-1).item())
e = int(outputs.end_logits.argmax(dim=-1).item())
print("Extracted Answer:", tokenizer.decode(inputs.input_ids[0][s : e + 1]).strip())

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License

Apache 2.0