ruri-v3-130m-coreml

CoreML conversion of cl-nagoya/ruri-v3-130m (a Japanese ModernBERT sentence-embedding model) for running the encoder on the Apple Neural Engine (ANE).

What this is

The ANE needs a fixed-shape, batch=1 model, and one model per sequence length. Those lengths live in a single bundle as four CoreML functions over one shared copy of the weights:

buckets-64-128-256-512.mlpackage    # seq_64, seq_128, seq_256, seq_512
tokenizer.json
config.json
1_Pooling/config.json

Only the encoder forward ((input_ids, attention_mask) -> last_hidden_state) is in CoreML; tokenization, prefixing, mean pooling and L2 normalization stay in the host application. Output matches the reference f32 path to fp16 rounding — measured 1 - cosine of 5.2e-6 (median over Japanese and English texts up to 512 tokens).

Requirements

• macOS 15 or newer. The conversion targets macOS15 / iOS 18 opsets.
• Apple Silicon, for the ANE to exist at all.
• For the kohagi path: kohagi 0.5.0 or newer, built with --features coreml. Earlier versions look for seq-<N>.mlpackage and will not find anything here.

Usage

kohagi

kohagi does the tokenizing, bucketing, pooling and normalization:

kohagi --device coreml --coreml-model-id takahashim/ruri-v3-130m-coreml \
       --prefix "検索文書: " < texts.jsonl

The first run compiles the bundle for the Neural Engine, which takes roughly 20 seconds; the result is cached under ~/Library/Caches/kohagi/coreml, so later runs start in well under a second.

You do not need this repository at all if you have kohagi 0.5.0: kohagi --device coreml converts cl-nagoya/ruri-v3-130m itself and caches the result. This repo is the shortcut — 260 MB of converted model against 503 MB of safetensors plus a 20-second conversion.

coremltools

Each function takes input_ids and attention_mask ([1, N] int32) and returns hidden ([1, N, 512] fp16). Pick the function by name and pad to its length.

import coremltools as ct, numpy as np
m = ct.models.MLModel("buckets-64-128-256-512.mlpackage", function_name="seq_128")
ids  = np.zeros((1, 128), np.int32); ids[0, :n]  = token_ids
mask = np.zeros((1, 128), np.int32); mask[0, :n] = 1
hidden = m.predict({"input_ids": ids, "attention_mask": mask})["hidden"][0]  # (128, 512)
vec = (hidden * mask[0, :, None]).sum(0) / mask.sum()
vec /= np.linalg.norm(vec)

Ruri expects a task prefix — "検索文書: " for documents, "検索クエリ: " for queries — prepended to the text before tokenizing.

Changes from the base model

• Converted to a CoreML ML Program with fp16 weights, targeting CPU_AND_NE.
• Four fixed sequence lengths as CoreML functions in one bundle, batch size 1.
• Weights and forward math are otherwise unchanged from cl-nagoya/ruri-v3-130m (revision e3114c6).

Converted with kohagi 0.5.0's own emitter (coreml-convert), which for this model produces byte-identical output to the coremltools route and the same Neural Engine placement.

License and attribution

Apache-2.0, inherited from the base model cl-nagoya/ruri-v3-130m (itself built on ModernBERT-Ja). This is a format conversion (a derivative work); all credit for the model belongs to the original authors. Please cite Ruri as requested on the base model card.