Text Retrieval
Transformers
Safetensors
English
qwen3
information-retrieval
LLM
Embedding
disaster-management
text-generation-inference
Instructions to use DMIR01/DMRetriever-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DMIR01/DMRetriever-4B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("DMIR01/DMRetriever-4B") model = AutoModel.from_pretrained("DMIR01/DMRetriever-4B") - Notebooks
- Google Colab
- Kaggle
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- Model Type: Text Embedding
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- Supported Languages: English
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- Number of Paramaters: 4B
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- Embedding Dimension: 1024
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For more details, including model training, benchmark evaluation, and inference performance, please refer to our [paper](https://www.arxiv.org/abs/2510.15087), [GitHub](https://github.com/KaiYin97/DMRETRIEVER).
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- Model Type: Text Embedding
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- Supported Languages: English
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- Number of Paramaters: 4B
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- Embedding Dimension: 2560
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For more details, including model training, benchmark evaluation, and inference performance, please refer to our [paper](https://www.arxiv.org/abs/2510.15087), [GitHub](https://github.com/KaiYin97/DMRETRIEVER).
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