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granite embedding 311m multilingual r2

Granite-Embedding-311M-Multilingual-R2 is a 311M-parameter dense embedding model from IBM's Granite Embeddings collection, licensed Apache-2.0. It produces 768-dimensional vectors and supports a context length of up to 32,768 tokens. The model is based on the ModernBERT architecture and supports 200+ languages, with enhanced support for 52 languages and programming code. It is released with ONNX and OpenVINO models and is tagged for sentence-transformers, feature extraction, and multilingual use.

EstimatedRepository opened Apr 20, 2026Source checked 9/26/2026Version: 44399559
LOCALRENTED GPUOPEN WEIGHTS
Freshness 50 out of 100. Repository age: 178 days old, Apr 20, 2026. Measures age, not quality.
Popularity 68 out of 100. 150,966 downloads in 30 days; 143 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

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Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

At a glance

Parameters
0.31B
Architecture
modernbert
Context length
32,768
License
apache-2.0
Disk space
0.6 GB
Software
vLLM
View the model source
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Best for

  • Multilingual text embeddings for retrieval and semantic similarity
  • Code retrieval for Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, and C++
  • Long-document search with up to 32,768 token context
  • Conversational multi-turn retrieval
  • Reasoning retrieval tasks

Tradeoffs

  • Explicit enhanced support covers only 52 languages and code, despite claims of 200+ language support
  • Performance may degrade when using Matryoshka dimension reduction below 768 dimensions
  • Training and evaluation details for all languages are not provided
  • The model card does not report numerical hardware requirements or latency benchmarks
  • No independent testing or validation is claimed in the source

Ways to run it

vLLM · Linux

Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.

vllm serve ibm-granite/granite-embedding-311m-multilingual-r2 --task embed
Official instructions

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