ONNX-quantized derivatives of 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 — 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:

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, evaluated using Korean-MTEB-Retrieval-Evaluators.

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.

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 — open an issue on this repo for questions.

Provenance and EU AI Act Art. 53 note

Upstream model: telepix/PIXIE-Rune-v1.0 — published by telepix.
Upstream licence: apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (ONNX). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.

cstr/PIXIE-Rune-v1.0-ONNX

PIXIE-Rune-v1.0 — ONNX Quantized Variants