metadata
language:
- ja
tags:
- embeddings
- retrieval
- transformer-free
- safetensors
- edge-ai
license: mit
base_model_relation: quantized
base_model:
- WangKaiLin/PipeOwl-1.5-jp
PipeOwl-1.8-jp-parameter-golf (Geometric Embedding)
A transformer-free semantic retrieval engine.
PipeOwl performs deterministic vocabulary scoring over a static embedding field:
score = α⋅base + β⋅Δfield
where:
- base = cosine similarity in embedding space
- Δfield = static scalar field bias
Features:
- O(n) over vocabulary.
- No attention.
- No transformer weights.
- CPU-friendly (<16MB model)
Architecture
- Static embedding table (V × D)
- Aligned vocabulary index
- Optional scalar bias field (Δfield)
- Linear scoring
- Pluggable decoder stage
- Targeted for CPU environments and low-latency systems (e.g. IME).
Model Specs
| item | value |
|---|---|
| vocab size | 26155 |
| embedding dim | 256 |
| storage format | safetensors (FP16) |
| model size | ~13.2 MB |
| languages | Japanese |
| startup time | <1s |
| query latency |
Quickstart
git clone https://huggingface.co/WangKaiLin/PipeOwl-1.8-jp-parameter-golf
cd PipeOwl-1.8-jp-parameter-golf
pip install numpy safetensors
python quickstart.py
Example:
Example semantic retrieval results:
Please enter words： 東京
Top-K Tokens:
1.000 | 東京
0.739 | 東京都
0.679 | 大阪
0.666 | ロンドン
0.646 | 名古屋
Please enter words： 大阪
Top-K Tokens:
1.000 | 大阪
0.756 | 関西
0.728 | 難波
0.717 | 京都
0.712 | 守口
Benchmark (CPU)
Environment:
- Vocab size: 26,155
- Embedding dimension: 256
- Hardware: CPU
Average query latency: maybe faster this is PipeOwl-1.6-jp benchmark
PipeOwl: 0.0036 sec BM25: 0.0421 sec Embedding: 0.0283 sec FAISS Flat: 0.0324 sec FAISS HNSW: 0.0230 sec
| Comparison | Speedup |
|---|---|
| vs BM25 | 11.7× faster |
| vs Embedding | 7.9× faster |
| vs FAISS Flat | 9.0× faster |
| vs FAISS HNSW | 6.4× faster |
PipeOwl shows 6–12× lower latency compared with common retrieval baselines in this setup.
Repository Structure
PipeOwl-1.8-jp-parameter-golf/
├ README.md
├ config.json
├ LICENSE
├ quickstart.py
├ engine.py
├ vocabulary.json
└ pipeowl_fp16.safetensors
LICENSE
MIT