# 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

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
Inference engine: CrispEmbed (ggml-based)
Conversion: convert-bert-embed-to-gguf.py

Provenance and EU AI Act Art. 53 note

Upstream model: intfloat/multilingual-e5-base — published by intfloat.
Upstream licence: mit. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository.
Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.