PipeOwl-1.0 (Geometric Embedding)

PipeOwl is a transformer-free geometric embedding package built on a static embedding field stored as NumPy arrays.

This repo provides:

• L1_base_embeddings.npy: float32 (V, 1024) embedding table (unit-normalized)
• L1_base_vocab.json: list of vocab strings aligned to embedding rows
• delta_base_scalar.npy: float32 (V,) optional scalar bias field
• minimal inference engine (engine.py) and usage script (quickstart.py)

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Attribution

The base embedding vectors were generated using BGE (Apache-2.0) via inference (model outputs). This repository does not redistribute any original BGE model weights.

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Quickstart

pip install numpy
python quickstart.py

Or minimal usage:

from engine import PipeOwlEngine, PipeOwlConfig

engine = PipeOwlEngine(PipeOwlConfig())
q = engine.encode("雪鴞好可愛")
# use q for similarity / retrieval

Files

• data/L1_base_embeddings.npy : embedding table (float32, V×1024)
• data/L1_base_vocab.json : vocab aligned with rows
• data/delta_base_scalar.npy : scalar bias (float32, V)
• engine.py : minimal runtime
• quickstart.py : example script

Notes

No safetensors / pytorch_model.bin is included because this model is distributed as a static NumPy embedding field.

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Parameter Size

~165M embedding parameters (static matrix)

Intended Use

• Semantic similarity
• Lightweight retrieval
• Geometric experimentation

Limitations

• No contextual modeling
• No token interaction modeling
• Domain performance varies

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Stress Test Results (Hard Retrieval Setting)

• corpus size = 1200
• eval size = 200
• ood ratio = 0.28

Model	in-domain MRR@10	OOD MRR@10
MiniLM	0.019	0.026
BGE	0.026	0.009
PipeOwl	0.013	0.023

Note: This test uses a harder corpus and adversarial-style queries. Absolute scores are low due to difficulty scaling.

See full experimental notes here: https://hackmd.io/@galaxy4552/BkpUEnTwbl

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pipeowl/
│
├─ README.md
├─ LICENSE
│
├─ engine.py
├─ quickstart.py
│   
└─ data/
    ├─ L1_base_embeddings.npy
    ├─ delta_base_scalar.npy
    └─ L1_base_vocab.json