Model reference · open weights

R3-rerank

Embeddings tencent Reranker 1 build Open weights 570 dl/mo

R3-rerank is an open-weight embedding model from tencent. R3-rerank-0.6b (FP32) weighs 1.2 GB; the smallest configuration that runs it is RTX 3060 12 GB.

  • R3-rerank is a 596M parameter cross-encoder model developed by Tencent for text-ranking tasks.
  • It functions as the reranking stage in a two-stage retriever designed for query-conditional agent skill retrieval, scoring query and skill pairs jointly.
  • The model is released under the Apache-2.0 license.

Summary of the tencent/R3-rerank-0.6b model card, 2026-10-01

What it is

Released bytencent
Released2026-07-08
Parameters596M
VRAM1.2 GB for the weights

What it runs on

Memory and cards for R3-rerank-0.6b (FP32)

1.2 GBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What tencent says about R3-rerank

Read the model card

The latest agent skill reranking model at the 0.6B scale. R3-Reranker is the cross-encoder (rerank) stage of R3-Skill's two-stage retriever for query-conditional agent skill retrieval. It scores each (query, skill) pair jointly, paired with R3-Embedding-0.6B for recall.

Usage

from sentence_transformers import CrossEncoder

model = CrossEncoder("tencent/R3-rerank-0.6b")
query = "I need to compose music"
skills = [  # The format is "name | description | skill_md"
    "music-composer | Composes original music | Creates music for various media formats ...",
    "music-lyricist | Writes lyrics for songs | Creates lyrics for various music genres ...",
    "music-editor | Edits and mixes music tracks | Provides audio editing and mixing services ...",
]
pairs = [(query, skill) for skill in skills]
scores = model.predict(pairs)
print(scores)
# [ 0.34937477 -1.7738094  -1.6604462 ]

Citation

@inproceedings{r3skill2026,
  title  = {Skill Is Not Document: A Query-Conditional Benchmark and Two-Stage Retriever for LLM Agent Skill Routing},
  author = {Wang, Zifei and Wen, Wei and Ji, Qiang and Qiao, Ruizhi and Sun, Xing},
  year   = {2026},
  url    = {https://arxiv.org/abs/2606.03565},
}

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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