candding

candding is an embedding library for Rust that implements every model architecture on candle's candle-core and candle-nn, with no ONNX runtime and no candle-transformers. It returns dense vectors, sparse rows, multi-vector matrices and rerank scores, and every catalog model is tested against its reference implementation, with a status per device and dtype.

cargo add candding

use candding::TextEmbedding;

fn main() -> candding::Result<()> {
  let model = TextEmbedding::builder("BAAI/bge-small-en-v1.5").build()?;
  let passages = model.passage_embed(&["Embeddings map text to vectors."], None)?;
  let queries = model.query_embed(&["what do embeddings do"], None)?;
  let score: f32 = queries[0].iter().zip(&passages[0]).map(|(q, p)| q * p).sum();
  println!("dim {} score {score:.4}", model.dim());
  Ok(())
}

What ships

Output	Entry point	Models
Dense vectors	`TextEmbedding`	35, in nine families: BERT, Qwen3, Gemma3, XLM-RoBERTa, MPNet, JinaBERT, NomicBERT, ModernBERT and GTE
Sparse `(index, weight)` rows	`SparseTextEmbedding`, `Bm25TextEmbedding`	6: `prithivida/Splade_PP_en_v1`, `naver/splade-code-06B`, `BAAI/bge-m3`, miniCOIL, BM42 and BM25
Multi-vector matrices for late interaction	`MultiVectorTextEmbedding`	4: `BAAI/bge-m3`, `colbert-ir/colbertv2.0`, `answerdotai/answerai-colbert-small-v1` and `jinaai/jina-colbert-v2`
One score per query and document	`TextCrossEncoder`	8: `cross-encoder/ms-marco-MiniLM-L6-v2`, `BAAI/bge-reranker-base` and `-v2-m3`, `jinaai/jina-reranker-v1-tiny-en` and `-v2-base-multilingual`, and `Qwen/Qwen3-Reranker` at 0.6B, 4B and 8B

The model catalog lists every model with its licence and its status per device and dtype.

Python

pip install candding

from candding import TextEmbedding

model = TextEmbedding("BAAI/bge-small-en-v1.5")
passages = model.passage_embed(["Embeddings map text to vectors."])
queries = model.query_embed(["what do embeddings do"])
print(model.dim, queries @ passages.T)

The bindings keep the Rust method names and return NumPy arrays. The wheels for Linux x86-64 and aarch64 and for macOS on Apple Silicon, with Metal, need no Rust toolchain; the Python guide has the API and the source build for CUDA.

Requirements

Requirement	Needed for
Linux or macOS	every build; Windows is not tested
Rust 1.94 or newer	every build
A C compiler	the default `download` feature, whose TLS stack builds `aws-lc-sys` from C; with `default-features = false` the builders read only the Hugging Face cache and local directories

Documentation

candding.rust-dd.com: getting started, Python, model catalog, API, CLI and roadmap.

License

candding is licensed under either Apache-2.0 or MIT, at your option.