Feature Extraction
Transformers
PyTorch
English
minicpmv
information retrieval
embedding model
visual information retrieval
custom_code
Instructions to use RhapsodyAI/MiniCPM-V-Embedding-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RhapsodyAI/MiniCPM-V-Embedding-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RhapsodyAI/MiniCPM-V-Embedding-preview", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RhapsodyAI/MiniCPM-V-Embedding-preview", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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@@ -185,3 +185,15 @@ If you find our work useful, please consider cite us:
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Thanks to MiniCPM-V-2.0 `arxiv.org/abs/2408.01800`, without which there won't be `minicpm-visual-embedding`.
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```
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Thanks to MiniCPM-V-2.0 `arxiv.org/abs/2408.01800`, without which there won't be `minicpm-visual-embedding`.
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# 中文文档
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https://www.53ai.com/news/RAG/2024082047058.html
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https://blog.csdn.net/bokesyo/article/details/141335670
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https://developer.aliyun.com/article/1590698?spm=a2c6h.13148508.setting.14.b4e94f0eIQp59B
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https://cloud.tencent.com/developer/article/2446218
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