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altaidevorg/
turkish-bge-m3-model2vec-turboquant-2bit

Name: turkish-bge-m3-model2vec-turboquant-2bit
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🇹🇷 Turkish BGE-M3 Model2Vec (TurboQuant 2-bit)

Extreme 2-bit INT2 quantized Turkish static sentence embedding model using Google TurboQuant (ICLR 2026) vector quantization.

Model Size: 2.50 MB (880x smaller than 2.2 GB BGE-M3)
Embedding Dimension: 256
Inference Speed: 20,013 sentences/sec on CPU
Accuracy (STSb-TR gorkem): 92.19% (Denoising effect enhances STS correlation)
Deployment: Ideal for in-browser WASM, smartwatches, IoT hardware, and embedded edge devices.

📊 Benchmark Comparison

Model	Quantization	Size	STSb-TR (`gorkemergune/stsb-tr`)	Speed
BAAI/bge-m3 (Teacher)	FP32	~2,200 MB	96.35%	79 sent/s
turkish-bge-m3-model2vec (Base)	FP16	19.36 MB	91.36%	63,012 sent/s
🥉 turkish-bge-m3-model2vec-turboquant-2bit	INT2 (2-bit)	2.50 MB	92.19%	20,013 sent/s

🚀 Usage

python

# Clone repo & install library from https://github.com/altaidevorg/model2vec_experiments
from src.turboquant import TurboQuantStaticModel

# Load 2-bit model directly
model = TurboQuantStaticModel.from_pretrained("altaidevorg/turkish-bge-m3-model2vec-turboquant-2bit")

sentences = ["Ultra hafif Türkçe vektör modeli."]
embeddings = model.encode(sentences)
print(f"Shape: {embeddings.shape}")  # (1, 256)

📄 License

Apache-2.0