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

- Xet hash:
- ceb62e5a52828cb5dd51eaf2441b81beca36932f05652f207497a3f4a792e835
- Size of remote file:
- 2.14 MB
- SHA256:
- 20d52b748525cd40b96e4ac6ae6fe33a5546a2ac600eb6ace4e504ea2a8a8c3a
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