granite-embedding-reranker-english-r2

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What granite-embedding-reranker-english-r2 is

granite-embedding-reranker-english-r2 Model Summary: granite-embedding-reranker-english-r2 is a 149M parameter dense cross-encoder model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 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 granite-embedding-reranker-english-r2 model uses a cross-encoder architecture to compute high-quality relevance scores between queries and documents by jointly encoding their text, enabling precise reranking based on contextual alignment. The model is trained with ranking-specific loss functions such as pListMLE, along with model merging techniques to enhance performance. The reranker model shows strong performance on standard information retrieval benchmark (BEIR, MIRACL), long-document search benchmarks (MLDR), and on many enterprise use cases. The latest granite embedding r2 release introduces two English embedding models, and one English reranking all 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. - granite-embedding-reranker-english-r2 (149M parameters): reranker model based on granite-embedding-english-r2, with an output embedding size of 768. Model Details - Developed by: Granite Embedding Team, IBM - Repository: ibm-granite/granite-embedding-models - Paper: Granite Embedding R2 Models - Language(s) (NLP): English - Release Date: Sep 8, 2025 - License: Apache 2.0 Usage The model is designed to compute relevance scores for query-document pairs, making it well-suited for reranking tasks in information retrieval and search applications. Usage with Sentence Transformers: The model is compatibl

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-reranker-english-r2 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (granite-embedding-reranker-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-reranker-english-r2","input":"text to embed"}'

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