---
last_updated: 2026-07-20
tags: [embedding, gte, alibaba, local, farsi, insurance, rag, lightweight]
source: import-knowledge
---

# Alibaba-NLP/gte-multilingual-base

## Overview

- **Type**: Local
- **Provider**: Alibaba NLP
- **Parameters**: ~305M
- **VRAM (fp16)**: ~610 MB
- **Embedding dimensions**: 768
- **Max input tokens**: 8,192
- **Language coverage**: 70+ languages **explicitly including Farsi** (fa)
- **Serving platform**: sentence-transformers, TEI
- **License**: Apache-2.0
- **Model card**: https://huggingface.co/Alibaba-NLP/gte-multilingual-base

## Why Consider It

Lightest model with **confirmed Farsi support**. Only 610 MB VRAM. 8K context. Apache-2.0 license. Benchmarked on MIRACL and PL-MTEB. Simpler than BGE-M3 (dense-only, no ColBERT/sparse overhead).

## Pros

- Explicitly lists Farsi in supported languages
- Only 610 MB VRAM — lightest meaningful option with Farsi
- 8,192 token context
- Benchmarked on MIRACL and PL-MTEB
- Apache-2.0 license
- Simpler architecture (dense-only) — easier to deploy and debug

## Cons

- No ColBERT/sparse support — dense-only retrieval
- No published isolated Farsi retrieval scores
- Alibaba NLP ecosystem less mature than HuggingFace ecosystem
- Not available on Ollama (may need manual GGUF conversion)

## API Test — Python

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Alibaba-NLP/gte-multilingual-base")

# Farsi insurance query
query = "شرایط لغو بیمه نامه چیست？"
embedding = model.encode(query)
print(f"Dimensions: {len(embedding)}")
# Expected: Dimensions: 768
```

## API Test — TEI (Docker)

```bash
# Start TEI server
docker run --gpus all -p 8080:80   -v $PWD/data:/data   ghcr.io/huggingface/text-embeddings-inference:latest   --model-id Alibaba-NLP/gte-multilingual-base

# Test
curl -s http://localhost:8080/embed   -H "Content-Type: application/json"   -d '{"inputs": "بیمه نامه شخص ثالث چیست؟"}'   | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d[0])}')"
```

## TODO

- [ ] Benchmark on MIRACL-Farsi subset
- [ ] Compare with BGE-M3 on Farsi retrieval quality
- [ ] Test GGUF conversion for Ollama compatibility
- [ ] Measure inference latency on RTX 3090