GRT-30M-Reranker (MatterWaveAI/grt-30m-reranker)

GRT-30M-Reranker (GRTForSequenceClassification) is a 100% Native Rotational Cross-Encoder Reranker (29,305,627 parameters, zero nn.Linear layers) compatible with both Hugging Face AutoModelForSequenceClassification and sentence-transformers CrossEncoder.

Reranking Benchmark Results

Model / Pipeline	Architecture	AllNLI Rerank MRR@10	AllNLI Rerank NDCG@10	Recall@1	Recall@3	STS-B Test Spearman (ρ)	Throughput (pairs/s)
MatterWaveAI/grt-30m (Base Unaligned)	GRTModel (circular pool)	37.99	52.12	20.5%	41.0%	32.51	120
MatterWaveAI/grt-30m-embedding (Bi-Encoder)	SentenceTransformer (circular)	52.56	63.74	32.5%	68.0%	62.42	152
MatterWaveAI/grt-30m-reranker (Cross-Encoder)	GRTForSequenceClassification (CrossEncoder)	56.93	67.13	38.5%	70.0%	61.75	123
Two-Stage Hybrid (grt-30m-embedding + grt-30m-reranker)	Bi-Encoder + Cross-Encoder Fusion	57.05	67.21	38.5%	70.0%	62.94	123

Quickstart (sentence-transformers CrossEncoder)

from sentence_transformers import CrossEncoder

reranker = CrossEncoder("MatterWaveAI/grt-30m-reranker")
query = "How do Givens rotations replace dense linear projections in a Transformer?"
documents = [
    "Cascaded 2D Givens rotations and butterfly permutations mix planar coordinate pairs with O(D log D) steps and zero nn.Linear layers.",
    "The stock market closed slightly higher on Tuesday after retail earnings reports.",
    "Standard Transformers rely on dense matrix multiplications (nn.Linear) in QKV and FFN projections.",
]
results = reranker.rank(query, documents, return_documents=True)
for r in results:
    print(f"{r['score']:.4f} | {r['text']}")

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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}
}