Model Summary: Granite-embedding-english-r2 is a 149M 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 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 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-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 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
Paper: Granite Embedding R2 Models
Language(s) (NLP): 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 efficient decoding, these models use Flash Attention 2. Installing it is optional, but can lead to faster inference.

pip install flash_attn==2.6.1

Usage with Sentence Transformers:

The model is compatible with SentenceTransformer library and is very easy to use:

First, install the sentence transformers library

pip install sentence_transformers

The model can then be used to encode pairs of text and find the similarity between their representations

python

from sentence_transformers import SentenceTransformer, util

model_path = "ibm-granite/granite-embedding-english-r2"
# Load the Sentence Transformer model
model = SentenceTransformer(model_path)

input_queries = [
    ' Who made the song My achy breaky heart? ',
    'summit define'
    ]

input_passages = [
    "Achy Breaky Heart is a country song written by Don Von Tress. Originally titled Don't Tell My Heart and performed by The Marcy Brothers in 1991. ",
    "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
    ]

# encode queries and passages. The model produces unnormalized vectors. If your task requires normalized embeddings pass normalize_embeddings=True to encode as below.
query_embeddings = model.encode(input_queries)
passage_embeddings = model.encode(input_passages)

# calculate cosine similarity
print(util.cos_sim(query_embeddings, passage_embeddings))

Usage with Huggingface Transformers:

This is a simple example of how to use the granite-embedding-english-r2 model with the Transformers library and PyTorch.

First, install the required libraries

pip install transformers torch

The model can then be used to encode pairs of text

python

import torch
from transformers import AutoModel, AutoTokenizer

model_path = "ibm-granite/granite-embedding-english-r2"

# Load the model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.eval()

input_queries = [
    ' Who made the song My achy breaky heart? ',
    'summit define'
    ]

# tokenize inputs
tokenized_queries = tokenizer(input_queries, padding=True, truncation=True, return_tensors='pt')

# encode queries
with torch.no_grad():
    # Queries
    model_output = model(**tokenized_queries)
    # Perform pooling. granite-embedding-278m-multilingual uses CLS Pooling
    query_embeddings = model_output[0][:, 0]

# normalize the embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, dim=1)

Evaluation Results

Granite embedding r2 models show a strong performance across tasks diverse tasks.

Performance of the granite models on MTEB Retrieval (i.e., BEIR), MTEB-v2, code retrieval (CoIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), benchmarks is reported in the below tables.

The r2 models demonstrates speed and efficiency, while mainintaining competitive performance. The average speed to encode documents on a single H100 GPU using a sliding window with 512 context length chunks is also reported.

Model	Parameters (M)	Embedding Size	BEIR Retrieval (15)	MTEB-v2 (41)	CoIR (10)	MLDR (En)	MTRAG (4)	Encoding Speed (docs/sec)
granite-embedding-125m-english	125	768	52.3	62.1	50.3	35.0	49.4	149
granite-embedding-30m-english	30	384	49.1	60.2	47.0	32.6	48.6	198
granite-embedding-english-r2	149	768	53.1	62.8	55.3	40.7	56.7	144
granite-embedding-small-english-r2	47	384	50.9	61.1	53.8	39.8	48.1	199

Model	Parameters (M)	Embedding Size	AVERAGE	MTEB-v2 Retrieval (10)	CoIR (10)	MLDR (En)	LongEmbed (6)	Table IR (5)	MTRAG(4)	Encoding Speed (docs/sec)
e5-base-v2	109	768	47.5	49.7	50.3	32.5	41.1	74.09	37.0	115
bge-base-en-v1.5	109	768	46.9	54.8	46.6	33.5	33.9	73.98	38.8	116
snowflake-arctic-embed-m-v2.0	305	768	51.4	58.4	52.2	32.4	55.4	80.75	29.2	106
gte-base-en-v1.5	137	768	52.8	55.5	42.4	42.7	59.4	80.52	36.0	116
gte-modernbert-base	149	768	57.5	57.0	71.5	46.2	57.0	76.68	36.8	142
nomic-ai/modernbert-embed-base	149	768	48.0	48.7	48.8	31.3	56.3	66.69	36.2	141

granite-embedding-english-r2	149	768	59.5	56.4	54.8	41.6	67.8	78.53	57.6	144
granite-embedding-small-english-r2	47	384	55.6	53.9	53.4	40.1	61.9	75.51	48.9	199

Model Architecture and Key Features

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 fewer layers and a smaller output embedding size (384), replacing granite-embedding-30m-english.

The following table shows the structure of the two models:

Model	granite-embedding-small-english-r2	granite-embedding-english-r2
Embedding size	384	768
Number of layers	12	22
Number of attention heads	12	12
Intermediate size	1536	1152
Activation Function	GeGLU	GeGLU
Vocabulary Size	50368	50368
Max. Sequence Length	8192	8192
# Parameters	47M	149M

Training and Optimization

The granite embedding r2 models incorporate key enhancements from the ModernBERT architecture, including:

Alternating attention lengths to accelerate processing
Rotary position embeddings for extended sequence length
A newly trained tokenizer optimized with code and text data
Flash Attention 2.0 for improved efficiency
Streamlined parameters, eliminating unnecessary bias terms

Data Collection

Granite embedding r2 models are trained using data from four key sources:

Unsupervised title-body paired data scraped from the web
Publicly available paired with permissive, enterprise-friendly license
IBM-internal paired data targetting specific technical domains
IBM-generated synthetic data

Notably, we do not use the popular MS-MARCO retrieval dataset in our training corpus due to its non-commercial license (many open-source models use this dataset due to its high quality).

The underlying encoder models using GneissWeb, an IBM-curated dataset composed exclusively of open, commercial-friendly sources.

For governance, all our data undergoes a data clearance process subject to technical, business, and governance review. This comprehensive process captures critical information about the data, including but not limited to their content description ownership, intended use, data classification, licensing information, usage restrictions, how the data will be acquired, as well as an assessment of sensitive information (i.e, personal information).

Infrastructure

We trained the granite embedding english r2 models using IBM's computing cluster, BlueVela Cluster, which is outfitted with NVIDIA H100 80GB GPUs. This cluster provides a scalable and efficient infrastructure for training our models over multiple GPUs.

Ethical Considerations and Limitations

Granite-embedding-english-r2 leverages both permissively licensed open-source and select proprietary data for enhanced performance. The training data for the base language model was filtered to remove text containing hate, abuse, and profanity. Granite-embedding-english-r2 is trained only for English texts, and has a context length of 8192 tokens (longer texts will be truncated to this size).

⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources

Citation

@misc{awasthy2025graniteembeddingr2models,
      title={Granite Embedding R2 Models}, 
      author={Parul Awasthy and Aashka Trivedi and Yulong Li and Meet Doshi and Riyaz Bhat and Vignesh P and Vishwajeet Kumar and Yushu Yang and Bhavani Iyer and Abraham Daniels and Rudra Murthy and Ken Barker and Martin Franz and Madison Lee and Todd Ward and Salim Roukos and David Cox and Luis Lastras and Jaydeep Sen and Radu Florian},
      year={2025},
      eprint={2508.21085},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.21085}, 
}