granite-embedding-multilingual-r2

Model Summary: Granite-Embedding-97M-Multilingual-R2 is a 97M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings at minimal compute cost. It produces 384-dimensional vectors with a context length of up to 32,768 tokens. The model supports 200+ languages (based on the multilingual pretraining corpus of the underlying encoder), with enhanced support for 52 languages and programming code that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets.

Granite Embedding 97M Multilingual R2 scores 60.3 on Multilingual MTEB Retrieval (18 tasks) — the highest retrieval score of any open multilingual embedding model under 100M parameters, outperforming the next-best model in its size class (multilingual-e5-small at 50.9) by +9.4 points — while being roughly 3× smaller than the full-size granite-embedding-311m-multilingual-r2. The multilingual R2 model shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks.

What's New in R2

• Architecture upgrade: ModernBERT replaces XLM-RoBERTa, bringing alternating attention, SiLU activations, and rotary position embeddings.
• Extended context: 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval.
• Compact multilingual vocabulary: A purpose-trained 180K-token tokenizer preserves broad multilingual coverage while reducing model size.
• Model pruning: Layer pruning (22 → 12 layers) from the full-size multilingual model, followed by continued distillation training to recover quality.
• Broader code coverage: Code retrieval training set that includes Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++.
• Training advances: Knowledge distillation from multiple teachers and contrastive fine-tuning yield a +14.6 point average gain over the previous-generation granite-embedding-107m-multilingual.
• Deployment flexibility: Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).

The model uses a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, code, and documents, enabling seamless comparison through cosine similarity. Built using contrastive fine-tuning, knowledge distillation, model pruning, and vocabulary selection, granite-embedding-97m-multilingual-r2 is optimized to ensure strong alignment between query and passage embeddings across many languages while maintaining a compact model size.

The Granite Embedding Multilingual R2 release consists of two multilingual embedding models, both based on the ModernBERT architecture:

• granite-embedding-311m-multilingual-r2 (311M parameters): with an output embedding size of 768, replacing granite-embedding-278m-multilingual. See granite-embedding-311m-multilingual-r2.
• granite-embedding-97m-multilingual-r2 (97M parameters): A reduced-size multilingual model built via layer pruning and vocabulary selection from the larger model, with fewer layers and a smaller output embedding size (384).

Model Details

• Developed by: Granite Embedding Team, IBM
• Repository: ibm-granite/granite-embedding-models
• Project Page: IBM Granite
• Paper: Granite Embedding Multilingual R2 Models
• Language(s) (NLP): 200+ languages supported, with enhanced support for 52 languages and programming code (see full language list)
• Release Date: April 29, 2026
• License: Apache 2.0

Supported Languages

The underlying encoder was pretrained on text from 200+ languages, and we report general-purpose embeddings for any of them. In addition, we provide enhanced support for 52 languages and programming code that receive explicit retrieval-pair and cross-lingual training data, producing higher-quality embeddings on retrieval tasks.

Albanian (sq), Arabic (ar), Azerbaijani (az), Bengali (bn), Bulgarian (bg), Catalan (ca), Chinese (zh), Croatian (hr), Czech (cs), Danish (da), Dutch (nl), English (en), Estonian (et), Finnish (fi), French (fr), Georgian (ka), German (de), Greek (el), Hebrew (he), Hindi (hi), Hungarian (hu), Icelandic (is), Indonesian (id), Italian (it), Japanese (ja), Kazakh (kk), Khmer (km), Korean (ko), Latvian (lv), Lithuanian (lt), Malay (ms), Marathi (mr), Norwegian (no), Persian (fa), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Serbian (sr), Slovak (sk), Slovenian (sl), Spanish (es), Swahili (sw), Swedish (sv), Tagalog (tl), Telugu (te), Thai (th), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi).

Additionally, the models are trained on programming code (Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++) and support cross-lingual code retrieval.

When to Use This Model

• Use granite-embedding-97m-multilingual-r2 for latency-sensitive production workloads, edge deployment, or when you need maximum encoding throughput with competitive multilingual quality. At 97M parameters, this model is 3× smaller than the full-size granite-embedding-311m-multilingual-r2 while preserving strong cross-lingual retrieval performance.
• Use granite-embedding-311m-multilingual-r2 when accuracy is the top priority, and you can afford the throughput of a 311M-parameter model.
• **Use [granite-embedding-english-r2](https://huggingface.co/ibm-granite/granite-embedding-engli