ibm-granite / feature-extraction updated 6 months ago

granite-embedding-small-english-r2

Name: granite-embedding-small-english-r2
Author: ibm-granite

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

3.8 M

Likes

Commercial use: allowed apache-2.0 Not gated SAFETENSORS 1 languages View on Hugging Face ↗

Download history

daily snapshots · 10 days

3.8 M3.3 M

Jul 28Jul 31Aug 3Aug 6

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

Compare with

bge-small-en-v1.5 72.4 M bge-large-en-v1.5 13.1 M bge-base-en-v1.5 10.0 M bge-small-zh-v1.5 4.8 M all-MiniLM-L6-v2 2.9 M bge-base-en-v1.5 2.2 M

Appears in

Best open coding models Commercial-use text models Models that fit in 12 GB