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granite embedding 97m multilingual r2

Granite-Embedding-97M-Multilingual-R2 is a 97M parameter dense embedding model from the Granite Embeddings collection, designed for multilingual text embeddings. It uses a ModernBERT-based bi-encoder architecture, produces 384-dimensional vectors, and supports context lengths up to 32,768 tokens. The model is released under the Apache-2.0 license and supports 200+ languages, with enhanced support for 52 languages and programming code. It is available with ONNX and OpenVINO formats and is compatible with sentence-transformers, vLLM, and llama.cpp (GGUF).

EstimatedRepository opened Apr 20, 2026Source checked 9/25/2026Version: 835ad140
LOCALRENTED GPUOPEN WEIGHTS
Freshness 50 out of 100. Repository age: 178 days old, Apr 20, 2026. Measures age, not quality.
Popularity 66 out of 100. 114,001 downloads in 30 days; 147 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
0.10B
Architecture
modernbert
Context length
32,768
License
apache-2.0
Disk space
0.2 GB
Software
vLLM
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Best for

  • Multilingual text embedding generation
  • Semantic search and information retrieval across languages
  • Code retrieval involving languages such as Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, and C++
  • Long-document retrieval and multi-passage search
  • Conversational multi-turn and reasoning retrieval tasks

Tradeoffs

  • No specific hardware requirements or performance benchmarks are stated in the supplied facts.
  • The model's compatibility is limited to the explicitly mentioned frameworks: sentence-transformers, vLLM, and llama.cpp (GGUF).
  • No information is provided about fine-tuning or custom training capabilities.
  • The model is not claimed to be tested by the summarizer, and no testing results are included.
  • The model produces 384-dimensional vectors, which may be insufficient for tasks requiring higher-dimensional embeddings.

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-97m-multilingual-r2 --task embed
Official instructions

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Popularity 90 out of 100. 4,849,199 downloads in 30 days; 2,697 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
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