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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# News
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- 2024-06-27: We released our first visual embedding model checkpoint minicpm-visual-embedding-v0 on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0).
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- 2024-05-08: We [open-sourced](https://github.com/bokesyo/minicpm-visual-embedding) our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code.
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# Get started
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# News
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- 2024-06-27: 🚀 We released our first visual embedding model checkpoint minicpm-visual-embedding-v0 on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0).
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- 2024-05-08: 🌍 We [open-sourced](https://github.com/bokesyo/minicpm-visual-embedding) our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code.
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# Get started
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