ruri-v3-310m
Ruri v3 is a general-purpose Japanese text embedding model built on top of ModernBERT-Ja. Ruri v3 offers several key technical advantages: - State-of-the-art performance for Japanese text embedding tasks. - Supports sequence lengths up to 8192 tokens - Previous versions of Ruri (v1, v2) were limited to 512. - Expanded vocabulary of 100K tokens, compared to 32K in v1 and v2 - The larger vocabulary make input sequences...
Params
310 M
Context
8,192
Downloads 30d
498 K
Likes
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Download history
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Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 1.3 GB | 1.9 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- ModernBertModel
- Parameters
- 310 M
- Tensor type
- F32
- Context length
- 8,192
- Vocabulary
- 102,400
- Layers / heads
- 25 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2025-04-09
- Training datasets
- cl-nagoya/ruri-v3-dataset-ft
- Added to our catalog
- 2026-07-28
Compare with any sentence-similarity model