🧠
Granite Embedding Small English R2 model by Ibm Granite
⭐ 45.3
🧠
Model

Granite Embedding Small English R2

by Ibm Granite ibm-granite/granite-embedding-small-english-r2
Free2AITools Nexus Index
45.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 52
P: Popularity 71
R: Recency 73
Q: Quality 65
Tech Context
0.05B Params
4.096K Ctx
Vital Performance
2.2M DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 45.3 FNI Score
Tiny 0.05B Params
4k Context
Hot 2.2M Downloads
8G GPU ~2GB Est. VRAM
Dense MODERNBERTMODEL Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID ibm-granite/granite-embedding-small-english-r2
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{ibm_granite_granite_embedding_small_english_r2,
  author = {Ibm Granite},
  title = {Granite Embedding Small English R2 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/ibm-granite/granite-embedding-small-english-r2}},
  note = {Accessed via Free2AITools.}
}
APA Style
Ibm Granite. (2026). Granite Embedding Small English R2 [Model]. Free2AITools. https://huggingface.co/ibm-granite/granite-embedding-small-english-r2

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run granite-embedding-small-english-r2
πŸ€— HF Download
huggingface-cli download ibm-granite/granite-embedding-small-english-r2
πŸ“¦ Install Lib
pip install -U transformers

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 52
Popularity (P) 71
Recency (R) 73
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Granite Embedding Small English R2: Authority (A:52), Popularity (P:71), Recency (R:73), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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Technical Deep Dive

⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source β†’

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
2.2MDownloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--ibm-granite--granite-embedding-small-english-r2
slug
ibm-granite--granite-embedding-small-english-r2
source
huggingface
author
Ibm Granite
license
Apache-2.0
tags
sentence-transformers, pytorch, safetensors, modernbert, feature-extraction, granite, embeddings, transformers, mteb, en, arxiv:2508.21085, license:apache-2.0, text-embeddings-inference, endpoints_compatible, deploy:azure, region:us

βš™οΈ Technical Specs

architecture
ModernBertModel
params billions
0.05
context length
4,096
pipeline tag
feature-extraction
vram gb
1.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
2,217,481
stars
0
forks
0

Data indexed from public sources. Updated daily.