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limbed

PyPI Version CI/CD Status PyPI - Downloads Python 3.10+ Platforms Architectures License: MIT

ColBERT embedding and MaxSim scoring without PyTorch. Uses a native C++ extension (ONNX Runtime + tokenizers-cpp) so you don't need to pull in 2 GB of deep learning dependencies just to encode some text.

Install

pip install limbed-py

Only runtime deps are numpy and huggingface-hub.

Usage

from limbed import LateEmbedder, compute_maxsim

model = LateEmbedder()  # downloads thlurte/mxbai-edge-colbert-v0-17m-onnx

q = model.encode_queries("What is late interaction?")
d = model.encode_docs("ColBERT computes token-level similarity.")

score = compute_maxsim(q[0], d[0])
print(score)

You can also point it at a local directory with model.onnx and tokenizer.json:

model = LateEmbedder("./my-model/")

Models

Alias Repo Size Dim Notes
mxbai-edge-colbert-v0-17m thlurte/mxbai-edge-colbert-v0-17m-onnx 66 MB 48 Default
mxbai-edge-colbert-v0-32m thlurte/mxbai-edge-colbert-v0-32m-onnx 124 MB 64
colbertv2.0 thlurte/colbertv2.0-onnx 438 MB 128 Standard ColBERTv2.0 BERT-based model
answerai-colbert-small-v1 thlurte/answerai-colbert-small-v1-onnx 135 MB 96 Lightweight, high-performance model
jina-colbert-v2 thlurte/jina-colbert-v2-onnx 2.23 GB 128 XLM-RoBERTa multilingual model
lateon thlurte/lateon-onnx 580 MB 128 Case-sensitive: use do_lower_case=False

Any ColBERT ONNX model should work if you put model.onnx and tokenizer.json in a folder and pass the path.

Benchmarks

The following benchmark was run on CPU using 20 queries (max length 32) and 20 documents (max length 256), comparing limbed against fastembed execution:

Performance (Throughput & Speedup)

Model Operation limbed Throughput fastembed Throughput Speedup (Wall-clock)
ColBERTv2.0 Queries 71.3 QPS 31.6 QPS 2.25x
ColBERTv2.0 Documents 93.8 DPS 66.1 DPS 1.42x
Jina ColBERT v2 Queries 6.2 QPS 5.0 QPS 1.25x
Jina ColBERT v2 Documents 10.1 DPS 5.2 DPS 1.94x

How it works

  • Tokenization and inference run in C++ via a nanobind extension
  • GIL is released during encode and MaxSim calls, so you can run multiple threads
  • Punctuation tokens are masked out of document embeddings (standard ColBERT behavior)
  • Embeddings are L2-normalized by default
  • CPU only for now

Docker & Alpine Linux Compatibility

Because the underlying precompiled ONNX Runtime library is linked against glibc, this package will not run out-of-the-box on Alpine Linux images (e.g., python:3.10-alpine).

If deploying via Docker, it is highly recommended to use a Debian-based slim image:

FROM python:3.10-slim

If you must use Alpine, you will need to install the compatibility layer: apk add --no-cache gcompat.

Supported Platforms & Architectures

Precompiled wheels are published to PyPI for the following environments:

Operating System Architecture Python Versions Notes
Linux x86_64, aarch64 3.10, 3.11, 3.12, 3.13, 3.14 Built on manylinux_2_28 (glibc-based)
macOS arm64 (Apple Silicon) 3.10, 3.11, 3.12, 3.13, 3.14 SDK/deployment target macOS 13.3+
Windows AMD64 (x86_64) 3.10, 3.11, 3.12, 3.13, 3.14

[!NOTE] Other platforms (such as Intel-based macOS or ARM-based Windows) will fall back to compilation from the source distribution (sdist). This requires a local C++ compiler (supporting C++17) and CMake.

License

MIT. See LICENSE.

Release files for limbed-py 0.1.6

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limbed_py-0.1.6-cp313-cp313-manylinux_2_28_x86_64.whl CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64 Details
limbed_py-0.1.6-cp313-cp313-manylinux_2_28_aarch64.whl CPython 3.13 CPython 3.13 Linux glibc 2.28+ ARM64 Details
limbed_py-0.1.6-cp313-cp313-macosx_13_0_arm64.whl CPython 3.13 CPython 3.13 macOS 13.0+ ARM64 Details
limbed_py-0.1.6-cp312-cp312-win_amd64.whl CPython 3.12 CPython 3.12 Windows x86-64 Details
limbed_py-0.1.6-cp312-cp312-manylinux_2_28_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64 Details
limbed_py-0.1.6-cp312-cp312-manylinux_2_28_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.28+ ARM64 Details
limbed_py-0.1.6-cp312-cp312-macosx_13_0_arm64.whl CPython 3.12 CPython 3.12 macOS 13.0+ ARM64 Details
limbed_py-0.1.6-cp311-cp311-win_amd64.whl CPython 3.11 CPython 3.11 Windows x86-64 Details
limbed_py-0.1.6-cp311-cp311-manylinux_2_28_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64 Details
limbed_py-0.1.6-cp311-cp311-manylinux_2_28_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.28+ ARM64 Details
limbed_py-0.1.6-cp311-cp311-macosx_13_0_arm64.whl CPython 3.11 CPython 3.11 macOS 13.0+ ARM64 Details
limbed_py-0.1.6-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details
limbed_py-0.1.6-cp310-cp310-manylinux_2_28_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64 Details
limbed_py-0.1.6-cp310-cp310-manylinux_2_28_aarch64.whl CPython 3.10 CPython 3.10 Linux glibc 2.28+ ARM64 Details
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