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Cataloged from tencent/R3-embedding-0.6b
All models by tencent →The latest agent skill retrieval model at the 0.6B scale. R3-Embedding is the bi-encoder (recall) stage of R3-Skill's two-stage retriever for query-conditional agent skill retrieval. It embeds a query and every skill independently and ranks candidates by cosine similarity, paired with R3-Rerank-0.6B for reranking.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("tencent/R3-embedding-0.6b")query_embedding = model.encode_query("I need to compose music")document_embeddings = model.encode_document([ # 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 ...",])similarities = model.similarity(query_embedding, document_embeddings)print(similarities)# tensor([[0.7410, 0.5510, 0.5028]])@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},}