…#1115)

## Description

Adds the `paraphrase-multilingual-MiniLM-L12-v2` sentence-transformer
model — the second multilingual embeddings model after distiluse,
completing #945. Ships **only the XNNPACK 8da4w variant** under
`MODEL_REGISTRY.ALL_MODELS` (see "Why a single variant" below).

384-d output, max 126 tokens, 50+ languages. Tokenizer is Unigram +
Precompiled normalizer + Metaspace decoder — **requires the bumped
`pytorch/extension/llm/tokenizers` runtime from #1114**, so this PR
blocks on that landing first and should be rebased onto main once #1114
merges.

HF repo:
[software-mansion/react-native-executorch-paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/software-mansion/react-native-executorch-paraphrase-multilingual-MiniLM-L12-v2)
(`v0.9.0` tag, layout mirrors distiluse).

**Why a single variant** — 
TLDR 8da4w works faster then all and was also one of the smallest,
without loss in precision.
Longer answer:
unlike distiluse, where Core ML fp32 won iPhone thanks to ANE
acceleration, benchmarks on iPhone 17 Pro + OnePlus 12 (~80-token input,
50 measured forwards after 3 warmups) showed the XNNPACK 8da4w variant
Pareto-dominates the other three on both platforms: faster than XNNPACK
fp32, Core ML fp32 *and* Core ML fp16 on iPhone, and ~36% smaller
steady-state memory footprint than the next-best variant. Likely cause:
paraphrase-multilingual-MiniLM-L12-v2 is a smaller model (~118 M params,
12 layers) where Core ML's runtime doesn't push enough work onto ANE for
the precision-conversion overhead to pay off. fp16 being slower than
fp32 on Core ML for this model is a tell that the runtime is falling
back to slower compute units. Shipping only `_8DA4W` keeps the public
surface aligned with the data; if a future Core ML or model update flips
the verdict, easy to add the other variants back.

**Memory methodology note** — the new paraphrase row in
`docs/docs/02-benchmarks/memory-usage.md` reports RSS / `phys_footprint`
deltas from a clean app baseline (loaded − idle), captured on-device at
the same conceptual point. The existing distiluse rows there (36 / 44
MB) come from an older measurement pass with a different (and not
reconstructable from the diff) methodology, so the two rows are not
directly comparable. A separate pass to re-measure distiluse and other
rows with the same methodology would be a good follow-up.

### Introduces a breaking change?

- [ ] Yes
- [x] No

### Type of change

- [ ] Bug fix (change which fixes an issue)
- [x] New feature (change which adds functionality)
- [ ] Documentation update (improves or adds clarity to existing
documentation)
- [ ] Other (chores, tests, code style improvements etc.)

### Tested on

- [x] iOS
- [x] Android

### Testing instructions

1. `cd apps/text-embeddings && npx expo run:ios` (or `run:android`).
2. Pick **"Multilingual Paraphrase (8da4w)"** in the model picker.
3. Add a sentence in one language, query with an aligned sentence in
another (e.g. Polish "Słoneczko" against "It's so sunny outside!"). The
cross-lingual pair should top the matches.

### Related issues

Closes the paraphrase-multilingual half of #945 (the distiluse half
landed in #1098).

### Checklist

- [x] I have performed a self-review of my code
- [x] I have commented my code, particularly in hard-to-understand areas
- [x] I have updated the documentation accordingly
- [x] My changes generate no new warnings

### Additional notes

Blocks on #1114.