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:
- 06dfcc159d2f4e909f8ac0f3fbede79708151b7039e2f47da4d35f83cb867ce7
- Size of remote file:
- 6.87 GB
- SHA256:
- fe8a272186c84997399fd6325cef707424d00b58668456197562c9e80e593667
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