BAAI/bge-multilingual-gemma2
feature-extraction model by BAAI
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From the publisher
Model card & documentation
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FlagEmbedding For more details please refer to our Github: FlagEmbedding. BGE-Multilingual-Gemma2 is a LLM-based multilingual embedding model. It is trained on a diverse range of languages and tasks based on google/gemma-2-9b. BGE-Multilingual-Gemma2 primarily demonstrates the following advancements: The training data of BGE-Multilingual-Gemma2 is available at this link. By default, FlagLLMModel will use all available GPUs when encoding. Please set os.environ["CUDAVISIBLEDEVICES"] to select specific GPUs. You also can set os.environ["CUDAVISIBLEDEVICES"]="" to make all GPUs unavailable. bge-multilingual-gemma2 exhibits state-of-the-art…
Read the full model card ↗ · Preview checked 2026-10-31T17:36:27.234Z
Inside the original model card — Document outline
- 📑 Open-source Plan
- Usage
- Using FlagEmbedding
- Using Sentence Transformers
- Using HuggingFace Transformers
- Evaluation
- Model List
- Citation
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Documentation belongs to its respective authors. Reported project/model license: gemma. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
BAAI/bge-multilingual-gemma2 is a feature-extraction model repository published by BAAI on Hugging Face. The source reports the sentence-transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- feature-extraction
- Library
- sentence-transformers
- Recent downloads (30 days)
- 99,684
- Cumulative likes
- 204
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 9,241,713,152
- Architecture
- Gemma2Model
- License
- gemma
- Access
- Not gated by Hugging Face
- Created
- 2024-07-25T16:55:46.000Z
- Last modified
- 2025-10-13T05:08:56.000Z
Compatibility and lineage
Language coverage was not reported.
Base-model lineage was not included.
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-10-30T06:52:24.484Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.