Fine-tuned from BAAI/bge-m3 with MatryoshkaLoss — supports dynamic dimension truncation at query time (1024 -> 768 -> 512 -> 256) via simple array slicing, with a single model loaded in memory (~571 MB).

Key Features

Dynamic dimensions: Truncate embeddings to 1024/768/512/256D at inference time — zero overhead
ONNX INT8: CPU-optimized, no GPU required (~571 MB RAM)
Matryoshka-trained: Quality degrades gracefully at lower dimensions (not random truncation)
Cross-lingual: FR/EN/DE/ES/IT/PT/ZH/JA/KO — inherited from BGE-M3
Drop-in replacement: Same 1024D output as gpahal/bge-m3-onnx-int8, with better discrimination (+6.6%)

Benchmark Results

Tested on 4 semantic pairs (FR/EN cross-lingual) + 2 negative pairs. Discrimination = avg_positive_similarity - avg_negative_similarity (higher = better separation).

Model	Dim	AvgPair	AvgNeg	Discrim	Throughput	Notes
m2v-bge-m3-1024d	1024	0.470	0.159	0.312	581 t/s	GPU, 20x faster but weaker cross-lingual
gpahal/bge-m3-onnx-int8	1024	0.695	0.317	0.377	28 t/s	CPU baseline
This model	1024	0.698	0.295	0.403	29 t/s	Best discrimination (+6.6% vs baseline)
This model @768D	768	0.711	0.315	0.397	29 t/s	-1.4% discrim, -25% storage
This model @512D	512	0.729	0.348	0.381	29 t/s	-5.4% discrim, -50% storage
This model @256D	256	0.658	0.200	0.458*	25 t/s	*Anomalous — loses cross-lingual

Per-Pair Breakdown (Cosine Similarity)

Pair	m2v-1024	onnx-1024	matr-1024	matr-768	matr-512	matr-256
couple_serrage (FR/EN)	0.056	0.351	0.284	0.321	0.371	0.197
fogg_depart (FR/FR)	0.634	0.837	0.866	0.873	0.878	0.825
revenue_q2 (EN/FR)	0.391	0.645	0.686	0.697	0.711	0.658
moteur_spec (FR/EN)	0.801	0.946	0.954	0.954	0.955	0.950

Dimension Recommendations

Dimension	vs M2V baseline	vs bge-m3-onnx-1024D	Storage (1M vectors)	Recommendation
1024D	+29.2%	+6.6%	4 GB	Maximum quality
768D	+27.2%	+5.3%	3 GB	Safe, -25% storage
512D	+22.1%	+1.1%	2 GB	Best for cloud CPU scaling
256D	anomalous	anomalous	1 GB	Not recommended (loses cross-lingual)

512D Matryoshka is BETTER than full-resolution bge-m3-onnx-int8 at 1024D (+1.1% discrimination) with half the storage and 2x faster cosine similarity.

Usage

With ONNX Runtime (Python)

python

from onnxruntime import InferenceSession
from transformers import AutoTokenizer
import numpy as np

tokenizer = AutoTokenizer.from_pretrained("tss-deposium/bge-m3-matryoshka-1024d-onnx-int8")
session = InferenceSession("model_quantized.onnx")

inputs = tokenizer("Bonjour le monde", return_tensors="np", padding=True, truncation=True)
outputs = session.run(None, dict(inputs))
embedding_1024d = outputs[0][0]  # [1024] float32

# Matryoshka truncation — just slice!
embedding_768d = embedding_1024d[:768]
embedding_512d = embedding_1024d[:512]
embedding_256d = embedding_1024d[:256]

With Deposium TurboV2 API

bash

# Full 1024D (default)
curl -X POST http://localhost:11435/api/embed \
  -d '{"model": "bge-m3-matryoshka", "input": "Bonjour le monde"}'

# Truncated to 512D (50% less storage, ~95% quality)
curl -X POST http://localhost:11435/api/embed \
  -d '{"model": "bge-m3-matryoshka", "input": "Bonjour", "dimensions": 512}'

Training Details

Base model: BAAI/bge-m3 (XLM-RoBERTa, 0.6B params)
Fine-tuning: MatryoshkaLoss + MultipleNegativesRankingLoss
Matryoshka dims: [1024, 768, 512, 256]
Dataset: 672,676 training samples / 35,405 eval samples
Epochs: 3, batch size 32, lr 2e-5, bf16
ONNX export: Dynamic INT8 quantization (avx512_vnni)
Size: ~571 MB (vs ~2.2 GB FP32)

Files

File	Description
`model_quantized.onnx`	ONNX INT8 quantized model
`config.json`	Model configuration
`ort_config.json`	ONNX Runtime configuration
`tokenizer.json`	Fast tokenizer
`tokenizer_config.json`	Tokenizer configuration
`sentencepiece.bpe.model`	SentencePiece BPE model
`special_tokens_map.json`	Special tokens mapping

License

MIT — commercial use allowed.