---
license: mit
base_model: intfloat/multilingual-e5-small
library_name: coreml
pipeline_tag: sentence-similarity
language: [en, ru, es]
tags: [coreml, sentence-embeddings, e5, macos]
---

# multilingual-e5-small, Core ML (int8)

[intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) converted to Core ML for
local file search on macOS (Seklu). Nothing else changed: same weights (int8-quantized), same tokenizer.

- `e5-small-coreml-v1.zip` (112,357,732 bytes,
  SHA-256 `ac5a88751994cce0844a96af3ff79bdb8dec100c3439057b176172a760f4c41a`), holding:
  - `E5Small.mlmodelc`: compiled Core ML model; inputs `input_ids`, `attention_mask` (int32, 1×16/32/64/128/256),
    output `embedding` (384, mean-pooled and L2-normalised inside the model). macOS 15+.
  - `vocab.tsv`: the SentencePiece Unigram vocabulary (piece TAB score, line number = id).
- Prefix texts with `query: ` or `passage: ` as the original model card says.
- Checked against PyTorch: cosine ≥ 0.9998 on EN/RU texts; a Swift tokenizer from `vocab.tsv` gave the same ids
  as Hugging Face `tokenizers` on 1511 of 1511 test strings.

License: MIT, as the original model (see `LICENSE`).