---
language:
- tr
license: apache-2.0
tags:
- turboquant
- quantization
- model2vec
- static-embeddings
- feature-extraction
- turkish
- bge-m3
pipeline_tag: feature-extraction
widget:
- text: "Hafif ve hızlı Türkçe vektör modelleri."
---

# 🇹🇷 Turkish BGE-M3 Model2Vec (TurboQuant 4-bit)

Ultra-compact 4-bit INT4 quantized Turkish static sentence embedding model using **Google TurboQuant (ICLR 2026)** vector quantization.

- **Model Size:** **4.92 MB** (447x smaller than 2.2 GB BGE-M3)
- **Embedding Dimension:** 256
- **Inference Speed:** **24,248 sentences/sec** on CPU
- **Accuracy (STSb-TR gorkem):** **91.79%** (Zero quality degradation vs FP16)
- **Deployment:** Ideal for mobile applications (iOS / Android) and local desktop apps.

---

## 📊 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-4bit** | **INT4 (4-bit)** | **4.92 MB** | **91.79%** | **24,248 sent/s** |

---

## 🚀 Usage

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

# Load 4-bit model directly
model = TurboQuantStaticModel.from_pretrained("altaidevorg/turkish-bge-m3-model2vec-turboquant-4bit")

sentences = ["Hafif vektör modelleri mobil cihazlarda mükemmel çalışır."]
embeddings = model.encode(sentences)
print(f"Shape: {embeddings.shape}")  # (1, 256)
```

---

## 📄 License
Apache-2.0