File size: 2,609 Bytes
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license: mit
language: [multilingual]
tags: [embeddings, gguf, ggml, text-embeddings, xlm-r, crispembed]
pipeline_tag: feature-extraction
base_model: intfloat/multilingual-e5-base
---
# multilingual-e5-base GGUF
GGUF format of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed).
Multilingual E5 Base. 100+ languages, 768-dimensional mean-pooled. Use prefix: "query: " / "passage: ".
## Files
| File | Quantization | Size |
|------|-------------|------|
| [multilingual-e5-base-q4_k.gguf](https://huggingface.co/cstr/multilingual-e5-base-GGUF/resolve/main/multilingual-e5-base-q4_k.gguf) | Q4_K | 247 MB |
| [multilingual-e5-base-q8_0.gguf](https://huggingface.co/cstr/multilingual-e5-base-GGUF/resolve/main/multilingual-e5-base-q8_0.gguf) | Q8_0 | 287 MB |
| [multilingual-e5-base.gguf](https://huggingface.co/cstr/multilingual-e5-base-GGUF/resolve/main/multilingual-e5-base.gguf) | F32 | 1066 MB |
## Quick Start
```bash
# Download
huggingface-cli download cstr/multilingual-e5-base-GGUF multilingual-e5-base-q4_k.gguf --local-dir .
# Run with CrispEmbed
./crispembed -m multilingual-e5-base-q4_k.gguf "Hello world"
# Or with auto-download
./crispembed -m multilingual-e5-base "Hello world"
```
## Model Details
| Property | Value |
|----------|-------|
| Architecture | XLM-R |
| Parameters | 278M |
| Embedding Dimension | 768 |
| Layers | 12 |
| Pooling | mean |
| Tokenizer | SentencePiece |
| Base Model | [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) |
## Verification
Verified bit-identical to HuggingFace sentence-transformers (cosine similarity >= 0.999 on test texts).
## Usage with CrispEmbed
CrispEmbed is a lightweight C/C++ text embedding inference engine using ggml.
No Python runtime, no ONNX. Supports BERT, XLM-R, Qwen3, and Gemma3 architectures.
```bash
# Build CrispEmbed
git clone https://github.com/CrispStrobe/CrispEmbed
cd CrispEmbed
cmake -S . -B build && cmake --build build -j
# Encode
./build/crispembed -m multilingual-e5-base-q4_k.gguf "query text"
# Server mode
./build/crispembed-server -m multilingual-e5-base-q4_k.gguf --port 8080
curl -X POST http://localhost:8080/v1/embeddings \
-d '{"input": ["Hello world"], "model": "multilingual-e5-base"}'
```
## Credits
- Original model: [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)
- Inference engine: [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed) (ggml-based)
- Conversion: `convert-bert-embed-to-gguf.py`
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