Model overview
**Model Summary:** Granite-Embedding-311M-Multilingual-R2 is a 311M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings. It produces 768-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 311M Multilingual R2 shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. The multilingual R2 model scores **65.2** on Multilingual MTEB Retrieval (18 tasks) — a **+13 point improvement** over granite-embedding-278m-multilingual (52.2) — and averages **56.3** across all retrieval benchmarks, representing a **+14.5 point gain** over the previous generation. It supports Matryoshka dimension reduction, 32k-token context, and ships with ONNX and OpenVINO models for production deployment. - **Architecture upgrade:** ModernBERT replaces XLM-RoBERTa, bringing alternating attention, GeGLU activations, and rotary position embeddings. - **Extended context:** 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval. - **Expanded vocabulary:** 262K multilingual tokenizer trained on text and code across 200+ languages. - **Matryoshka support:** Truncate embeddings to 512, 384, 256, or 128 dimensions with graceful degradation. - **Broader code coverage:** Code retrieval supported for Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++. - **Training advances:** Knowledge distillation from multiple teachers, contrastive fine-tuning, and model merging yield +14.5 points on average retrieval benchmarks. - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
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Source prices & cost analysis
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Estimate monthly base token costs
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Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
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Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。