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vectorlink
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multilingual-e5-base-GGUF

Feature Extraction
GGUF
sentence-similarity
embeddings
Model card Files
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How to use from
llama.cpp
Install (macOS, Linux) 
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible embeddings server:
llama serve -hf vectorlink/multilingual-e5-base-GGUF:Q8_0 --embeddings
Install from WinGet (Windows) 
winget install llama.cpp
# Start a local OpenAI-compatible embeddings server:
llama serve -hf vectorlink/multilingual-e5-base-GGUF:Q8_0 --embeddings
Use pre-built binary 
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible embeddings server:
./llama-server -hf vectorlink/multilingual-e5-base-GGUF:Q8_0 --embeddings
Build from source code 
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible embeddings server:
./build/bin/llama-server -hf vectorlink/multilingual-e5-base-GGUF:Q8_0 --embeddings
Quick Links

multilingual-e5-base — GGUF (Q8_0)

GGUF build of intfloat/multilingual-e5-base — a multilingual XLM-RoBERTa embedding model (278M params, 768 dimensions, 100 languages, passage:/query: asymmetric prefixes).

Built from first-party intfloat safetensors, because no reputable quantizer publishes this model — this repo exists so downstream projects (vectorlink, TerminusDB) have an auditable supply chain rather than pulling a random community upload.

Provenance

Step Tool Pin
Source weights intfloat/multilingual-e5-base commit d128750597153bb5987e10b1c3493a34e5a4502a
Conversion llama.cpp convert_hf_to_gguf.py release b6987
Quantization llama-quantize → Q8_0 llama.cpp 0.6.0

Build script: make-multilingual-e5-base-gguf.sh — downloads pinned first-party weights, verifies the safetensors SHA-256, converts, asserts mean pooling + 768-dim in the GGUF metadata, then quantizes.

Verification

Same probe text, cosine similarity vs the original safetensors weights (f16 conversion): 0.9998. Mean pooling, passage:/query: prefixes applied by the caller per E5 convention.

Usage (Ollama) 

ollama pull hf.co/vectorlink/multilingual-e5-base-GGUF:Q8_0
ollama cp hf.co/vectorlink/multilingual-e5-base-GGUF:Q8_0 multilingual-e5-base
curl localhost:11434/api/embed -d '{"model":"multilingual-e5-base","input":"query: test"}'  # → 768-dim

License

MIT — inherited from intfloat/multilingual-e5-base. This repo performs format conversion and quantization only; no training, fine-tuning, or modification of weights. See LICENSE.

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