granite-embedding-small-english-r2

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What granite-embedding-small-english-r2 is

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 permissive, enterprise-friendly license, plus IBM collected and generated datasets. The r2 models show strong performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), and on many enterprise use cases. These models use a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite-embedding-small-english-r2 is optimized to ensure strong alignment between query and passage embeddings. The latest granite embedding r2 release introduces two English embedding models, both based on the ModernBERT architecture: - granite-embedding-english-r2 (149M parameters): with an output embedding size of 768, replacing granite-embedding-125m-english. - granite-embedding-small-english-r2 (47M parameters): A first-of-its-kind reduced-size model, with 8192 context length support, fewer layers and a smaller output embedding size (384), replacing granite-embedding-30m-english. Model Details - Developed by: Granite Embedding Team, IBM - Repository: ibm-granite/granite-embedding-models - Project Page: IBM Granite - Paper: Granite Embedding R2 Models - Language(s): English - Release Date: Aug 15, 2025 - License: Apache 2.0 Usage Intended Use: The model is designed to produce fixed length vector representations for a given text, which can be used for text similarity, retrieval, and search applications. For effi

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys granite-embedding-small-english-r2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (granite-embedding-small-english-r2 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"granite-embedding-small-english-r2","input":"text to embed"}'

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