Model reference · open weights
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.
Summary of the tencent/R3-rerank-0.6b model card, 2026-10-01
What it is
| Released by | tencent |
|---|---|
| Released | 2026-07-08 |
| Parameters | 596M |
| VRAM | 1.2 GB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From 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.
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 ]
@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.