license: mit
base_model: intfloat/multilingual-e5-base
library_name: gguf
tags:
- feature-extraction
- sentence-similarity
- embeddings
- gguf
pipeline_tag: feature-extraction
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.