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candding

Run dense, sparse, multi-vector and reranking models from Python on Rust's candle, with no ONNX runtime. Every model in the catalog is checked against its reference implementation, and every result comes back as a NumPy array.

pip install candding
import candding

model = candding.TextEmbedding("BAAI/bge-small-en-v1.5")
docs = model.passage_embed(["Embeddings map text to vectors.", "Rust is a systems language."])
query = model.query_embed(["what do embeddings do"])
print(query @ docs.T)

reranker = candding.TextCrossEncoder("BAAI/bge-reranker-base")
print(reranker.rerank("what do embeddings do", ["Embeddings map text to vectors.", "Rust is a systems language."]))
Platform Devices
Linux x86-64 and aarch64 CPU
macOS on Apple Silicon CPU and Metal (device="auto" picks Metal)

Wheels serve CPython 3.9 and newer. Models download from the Hugging Face Hub into its standard cache on first use, and HF_TOKEN unlocks a gated model.

Licensed under Apache-2.0 or MIT, at your option.

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Release files for candding 0.1.0

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Source distribution for candding 0.1.0
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candding-0.1.0-cp39-abi3-manylinux_2_28_x86_64.whl CPython 3.9 abi3 Linux glibc 2.28+ x86-64 Details
candding-0.1.0-cp39-abi3-manylinux_2_28_aarch64.whl CPython 3.9 abi3 Linux glibc 2.28+ ARM64 Details
candding-0.1.0-cp39-abi3-macosx_11_0_arm64.whl CPython 3.9 abi3 macOS 11.0+ ARM64 Details

Total release size: 21.8 MB

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