README.md

---
language:
  - multilingual
  - ko
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - onnx
  - quantized
  - xlm-roberta
  - dense-encoder
  - dense
  - fastembed
base_model: telepix/PIXIE-Rune-v1.0
pipeline_tag: feature-extraction
---

# PIXIE-Rune-v1.0 — ONNX Quantized Variants

ONNX-quantized derivatives of [telepix/PIXIE-Rune-v1.0](https://huggingface.co/telepix/PIXIE-Rune-v1.0),
an encoder-based multilingual embedding model developed by TelePIX Co., Ltd. optimized for semantic
retrieval across 74 languages with specialization in Korean/English aerospace domain applications.

> **Original model:** [`telepix/PIXIE-Rune-v1.0`](https://huggingface.co/telepix/PIXIE-Rune-v1.0) —
> safetensors weights + FP32 ONNX (`onnx/model.onnx` + `onnx/model.onnx_data`).
> This repo adds INT8 and INT4 quantized ONNX variants for CPU-efficient deployment.

---

## Model Description

| Property | Value |
|---|---|
| Base model | `telepix/PIXIE-Rune-v1.0` (XLM-RoBERTa-large) |
| Architecture | Transformer encoder |
| Output dimensionality | 1024 |
| Pooling | Mean pooling + L2 normalize |
| Max sequence length | 6,000 tokens |
| Languages | 74 (XLM-RoBERTa vocabulary: 250,002 tokens) |
| Domain | General multilingual + aerospace specialization |
| License | Apache 2.0 |

---

## ONNX Variants

| File | Quantization | Size | Avg cos vs FP32 | Pearson r | MRR | Notes |
|---|---|---|---|---|---|---|
| `onnx/model_quantized.onnx` | INT8 dynamic | 542 MB | 0.969 | 0.998 | 1.00 | `quantize_dynamic`, all weights |
| `onnx/model_int4.onnx` | INT4 + INT8 emb | 434 MB | 0.941 | 0.998 | 1.00 | `MatMulNBits` + INT8 Gather |
| `onnx/model_int4_full.onnx` | INT4 full | 337 MB | 0.941 | 0.998 | 1.00 | `MatMulNBits` + INT4 Gather (opset 21) |

**Metrics** measured on 8 semantically diverse sentences vs FP32 reference.
Pearson r = correlation of pairwise cosine similarity matrices (structure preservation).
MRR = Mean Reciprocal Rank on a retrieval probe — 1.00 = perfect retrieval ranking preserved.

### Quantization methodology

The XLM-RoBERTa vocabulary has 250,002 tokens × 1024 dimensions, making the word embedding
table the dominant weight (~977 MB FP32). Each variant handles it differently:

- **INT8** (`model_quantized.onnx`): `onnxruntime.quantization.quantize_dynamic(weight_type=QInt8)` —
  quantizes all weight tensors including the embedding Gather to INT8. Compact, maximum compatibility.
- **INT4 + INT8 emb** (`model_int4.onnx`): Two-pass.
  Pass 1: `MatMulNBitsQuantizer(block_size=32, symmetric=True)` packs transformer MatMul weights
  to 4-bit nibbles. Pass 2: `quantize_dynamic(op_types=["Gather"], weight_type=QInt8)` brings
  the embedding table from 977 MB FP32 → 244 MB INT8.
- **INT4 full** (`model_int4_full.onnx`): Same MatMulNBits pass, then manual
  `DequantizeLinear(axis=0)` node insertion packs the embedding table as per-row symmetric
  INT4 nibbles (scale = max(|row|)/7). Requires opset upgrade 14→21. Embedding: 977 MB → 122 MB.

---

## Usage

### fastembed (Rust)

This repo is integrated in [fastembed-rs](https://github.com/Anush008/fastembed-rs):

```rust
use fastembed::{EmbeddingModel, InitOptions, TextEmbedding};

// INT8 — most compatible, 542 MB
let model = TextEmbedding::try_new(InitOptions::new(EmbeddingModel::PixieRuneV1Q))?;

// INT4 + INT8 embedding — 434 MB
let model = TextEmbedding::try_new(InitOptions::new(EmbeddingModel::PixieRuneV1Int4))?;

// INT4 full — smallest, 337 MB
let model = TextEmbedding::try_new(InitOptions::new(EmbeddingModel::PixieRuneV1Int4Full))?;

let embeddings = model.embed(vec!["안녕하세요", "Hello world"], None)?;
```

### ONNX Runtime (Python)

```python
import onnxruntime as ort
import numpy as np
from tokenizers import Tokenizer

tokenizer = Tokenizer.from_file("tokenizer.json")
tokenizer.enable_truncation(max_length=512)
tokenizer.enable_padding(pad_token="<pad>", pad_id=1)

session = ort.InferenceSession("onnx/model_quantized.onnx",
                                providers=["CPUExecutionProvider"])

texts = ["텔레픽스는 어떤 산업 분야에서 위성 데이터를 활용하나요?",
         "텔레픽스는 해양, 자원, 농업 등 다양한 분야에서 위성 데이터를 분석하여 서비스를 제공합니다."]

enc  = tokenizer.encode_batch(texts)
ids  = np.array([e.ids            for e in enc], dtype=np.int64)
mask = np.array([e.attention_mask for e in enc], dtype=np.int64)

out = session.run(None, {"input_ids": ids, "attention_mask": mask})[0]  # (batch, seq, 1024)

# Mean pooling + L2 normalize
pooled = (out * mask[..., None]).sum(1) / mask.sum(1, keepdims=True).clip(1e-9)
norms  = np.linalg.norm(pooled, axis=-1, keepdims=True)
embeddings = pooled / norms.clip(1e-12)
# cosine similarity
scores = embeddings @ embeddings.T
print(scores)
```

### sentence-transformers (original FP32 weights)

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("telepix/PIXIE-Rune-v1.0")

queries   = ["텔레픽스는 어떤 산업 분야에서 위성 데이터를 활용하나요?",
             "국방 분야에 어떤 위성 서비스가 제공되나요?"]
documents = ["텔레픽스는 해양, 자원, 농업 등 다양한 분야에서 위성 데이터를 분석하여 서비스를 제공합니다.",
             "정찰 및 감시 목적의 위성 영상을 통해 국방 관련 정밀 분석 서비스를 제공합니다."]

q_emb = model.encode(queries,   prompt_name="query")
d_emb = model.encode(documents)
scores = model.similarity(q_emb, d_emb)
print(scores)
```

---

## Quality Benchmarks (original model)

Results from [telepix/PIXIE-Rune-v1.0](https://huggingface.co/telepix/PIXIE-Rune-v1.0),
evaluated using [Korean-MTEB-Retrieval-Evaluators](https://github.com/nlpai-lab/KURE/tree/main/evaluation).

### 6 Datasets of MTEB (Korean)

| Model | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
|---|---|---|---|---|---|---|
| telepix/PIXIE-Spell-Preview-1.7B | 1.7B | 0.7567 | 0.7149 | 0.7541 | 0.7696 | 0.7882 |
| telepix/PIXIE-Spell-Preview-0.6B | 0.6B | 0.7280 | 0.6804 | 0.7258 | 0.7448 | 0.7612 |
| **telepix/PIXIE-Rune-v1.0** | **0.5B** | **0.7383** | **0.6936** | **0.7356** | **0.7545** | **0.7698** |
| telepix/PIXIE-Splade-Preview | 0.1B | 0.7253 | 0.6799 | 0.7217 | 0.7416 | 0.7579 |
| nlpai-lab/KURE-v1 | 0.5B | 0.7312 | 0.6826 | 0.7303 | 0.7478 | 0.7642 |
| BAAI/bge-m3 | 0.5B | 0.7126 | 0.6613 | 0.7107 | 0.7301 | 0.7483 |
| Snowflake/snowflake-arctic-embed-l-v2.0 | 0.5B | 0.7050 | 0.6570 | 0.7015 | 0.7226 | 0.7390 |
| Qwen/Qwen3-Embedding-0.6B | 0.6B | 0.6872 | 0.6423 | 0.6833 | 0.7017 | 0.7215 |
| jinaai/jina-embeddings-v3 | 0.5B | 0.6731 | 0.6224 | 0.6715 | 0.6899 | 0.7088 |
| openai/text-embedding-3-large | N/A | 0.6465 | 0.5895 | 0.6467 | 0.6646 | 0.6853 |

Benchmarks: Ko-StrategyQA, AutoRAGRetrieval, MIRACLRetrieval, PublicHealthQA, BelebeleRetrieval, MultiLongDocRetrieval.

### 7 Datasets of BEIR (English)

| Model | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
|---|---|---|---|---|---|---|
| Snowflake/snowflake-arctic-embed-l-v2.0 | 0.5B | 0.5812 | 0.5725 | 0.5705 | 0.5811 | 0.6006 |
| **telepix/PIXIE-Rune-v1.0** | **0.5B** | **0.5781** | **0.5691** | **0.5663** | **0.5791** | **0.5979** |
| telepix/PIXIE-Spell-Preview-1.7B | 1.7B | 0.5630 | 0.5446 | 0.5529 | 0.5660 | 0.5885 |
| Qwen/Qwen3-Embedding-0.6B | 0.6B | 0.5558 | 0.5321 | 0.5451 | 0.5620 | 0.5839 |
| Alibaba-NLP/gte-multilingual-base | 0.3B | 0.5541 | 0.5446 | 0.5426 | 0.5574 | 0.5746 |
| BAAI/bge-m3 | 0.5B | 0.5318 | 0.5078 | 0.5231 | 0.5389 | 0.5573 |
| jinaai/jina-embeddings-v3 | 0.6B | 0.4482 | 0.4116 | 0.4379 | 0.4573 | 0.4861 |

Benchmarks: ArguAna, FEVER, FiQA-2018, HotpotQA, MSMARCO, NQ, SCIDOCS.

---

## License

Apache 2.0 — same as the original [telepix/PIXIE-Rune-v1.0](https://huggingface.co/telepix/PIXIE-Rune-v1.0).

## Citation

```bibtex
@software{TelePIX-PIXIE-Rune-v1,
  title  = {PIXIE-Rune-v1.0},
  author = {TelePIX AI Research Team and Bongmin Kim},
  year   = {2025},
  url    = {https://huggingface.co/telepix/PIXIE-Rune-v1.0}
}
```

## Contact

Original model authors: bmkim@telepix.net
ONNX quantization: [cstr](https://huggingface.co/cstr) — open an issue on this repo for questions.