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
Browse files
README.md
CHANGED
|
@@ -19,7 +19,7 @@ Our model is capable of:
|
|
| 19 |
|
| 20 |
- Help you read a long visually-intensive or text-oriented PDF document and find the pages that answer your question.
|
| 21 |
|
| 22 |
-
- Help you build a personal library and
|
| 23 |
|
| 24 |
- It has only 2.8B parameters, and has the potential to run on your PC.
|
| 25 |
|
|
|
|
| 19 |
|
| 20 |
- Help you read a long visually-intensive or text-oriented PDF document and find the pages that answer your question.
|
| 21 |
|
| 22 |
+
- Help you build a personal library and retrieve book pages from a large collection of books.
|
| 23 |
|
| 24 |
- It has only 2.8B parameters, and has the potential to run on your PC.
|
| 25 |
|