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AI 模型库/ibm-granite/granite-embedding-311m-multilingual-r2
MODEL PROFILE · 模型档案

granite-embedding-311m-multilingual-r2

ibm-granite · granite-embedding-311m-multilingual-r2

**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).

译文排队中，当前展示来源原文。

向量与相似度license:apache-2.0
上下文窗口来源未披露
输入 → 输出文本 → 向量
模型权重待核验
档案更新时间2026/11/2 19:16:11
OVERVIEW

模型介绍

**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).

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

PRICING & COST

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

来源 / 渠道输入输出计费单位采集时间
暂未取得可核验报价，请查阅来源渠道。

按月估算基础 Token 费用

—

预估不包含缓存优惠、阶梯定价、多模态附加费用、网络检索、税费及推理额外输出。

INDEPENDENT BENCHMARKS

公开评测与运行配置

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。

查看完整已收录榜单 →

常见问题

开放权重是否代表可以免费商用？

不一定。应核对具体版本许可证、商用门槛和使用政策。自部署还会产生算力和维护成本。

这些价格是实时结算价格吗？

不是实时结算价格，而是带采集时间的来源报价快照。请到对应渠道核对当前版本、区域、折扣和计费方式。

资料来源与可追溯记录

huggingface · ibm-granite/granite-embedding-311m-multilingual-r2 ↗2026/10/29 21:45:56
huggingface · enrich:566e1526-81c3-4c06-a821-d9a636bb3da3 ↗2026/10/29 05:12:35

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。