| --- |
| 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](https://huggingface.co/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`](https://github.com/terminusdb-org/vectorlink/blob/main/scripts/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) |
| |
| ```bash |
| 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`. |
| |
