granite-embedding-small-english-r2
Model Summary: Granite-embedding-small-english-r2 is a 47M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissi...
Params
50 M
Context
8,192
Downloads 30d
6.9 M
Likes
113
Download history
daily snapshots · 106 days
▲ 1.7 M in the last 30 days (33.4%)
6.9 M5.2 M
Oct 12Oct 22Nov 1Nov 10
6.9 M3.8 M
Aug 13Sep 12Oct 12Nov 10
6.9 M3.3 M
Jul 28Sep 1Oct 6Nov 10
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 | bf16 | 0.1 GB | 0.6 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
- 50 M
- Tensor type
- BF16
- Context length
- 8,192
- Vocabulary
- 50,368
- Layers / heads
- 12 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2025-07-17
- Training datasets
- undisclosed
- Added to our catalog
- 2026-07-28
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