GRT-30M-VL (MatterWaveAI/grt-30m-vl)

GRT-30M-VL is a 100% Native Rotational Vision-Language Model (34.26M total parameters, zero nn.Linear dense weight matrices across both the vision encoder and language backbone) built on top of MatterWaveAI/grt-30m.

Architecture Summary

Component	Module	Parameters	Geometry
Patch Stem	GRTPolarPatchEmbedding	20,992	14x14 unfold → GRTPearlNorm → 11-stage Givens butterfly cascade → 256-dim (128 planar pairs)
2D Positional Encoding	GRTRotaryEmbedding2D	128	2D axial rotary encoding (64 row pairs + 64 column pairs) over the 16x16 patch grid
Vision Backbone	6x GRTDecoderLayer (is_causal=False)	4,877,074	Bidirectional multi-head rotational attention (n_qkv_rotations=11) + Clifford mixer + 21-stage rotational FFN
Spatial Merger	GRTSpatialRotationalMerger	56,000	2x2 spatial pixel-unshuffle (256 → 64 tokens) + 21-stage Givens expand/RadialGELU/contract projector
Vision Tower Total	GRTVisionModel (model.visual)	4,954,194	0 nn.Linear layers (224x224 RGB → 64 visual planar-pair tokens of dim 256)
Language Backbone	GRTModel + GRTCosineHead	29,305,498	12-layer causal rotational language model (MatterWaveAI/grt-30m, tied polar embeddings & cosine head)
Total (GRT-30M-VL)	GRTVLForConditionalGeneration	34,259,692	100% Rotational Multimodal Transformer

Quickstart (transformers)

from PIL import Image
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "MatterWaveAI/grt-30m-vl"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, dtype=torch.float32)

image = Image.new("RGB", (224, 224), color=(225, 45, 45))
inputs = processor(text="<|image_pad|>Describe this image:", images=image, return_tensors="pt")

out_ids = model.generate(**inputs, max_new_tokens=20, do_sample=False)
print(processor.batch_decode(out_ids, skip_special_tokens=True)[0])

Alignment & Evaluation Summary

• Held-out Visual Captioning Perplexity: 19217.10 (unaligned) → 1.26 (aligned)
• Held-out Visual Attribute Accuracy:
  • Color recognition: 100.0%
  • Shape recognition: 25.0%
  • Spatial position recognition: 58.3%
  • Exact caption match: 12.5%

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Complete MatterWaveAI/grt-30m* Research Suite on Hugging Face

1. Base Causal LM: MatterWaveAI/grt-30m
2. Chat Fine-Tune: MatterWaveAI/grt-30m-chat
3. 4-Expert Mixture-of-Rotations (MoR): MatterWaveAI/grt-30m-mor
4. Circular-Pooled Sentence Embedding: MatterWaveAI/grt-30m-embedding
5. Cross-Encoder Reranker: MatterWaveAI/grt-30m-reranker
6. Vision-Language (GRTVL): MatterWaveAI/grt-30m-vl
7. Canonical Tokenizer & Polar Embedding Bank: MatterWaveAI/grt-30m-tokenizer

• Whitepaper: The Geometric Rotation Transformer (GRT): A Purely Rotational Foundation Architecture (GRT_Whitepaper.pdf)

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License

Apache 2.0

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Citation

@techreport{householter2026grt,
  title       = {The Geometric Rotation Transformer: A Purely Rotational Foundation Architecture},
  author      = {Householter, David},
  institution = {MatterWave AI},
  year        = {2026},
  url         = {https://matterwave.ai}
}