Visual Question Answering
Transformers
PyTorch
English
minicpmv
feature-extraction
GUI
Agent
minicpm
custom_code
Instructions to use RhapsodyAI/minicpm-guidance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RhapsodyAI/minicpm-guidance with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="RhapsodyAI/minicpm-guidance", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RhapsodyAI/minicpm-guidance", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Differences between minicpm v2.0 and minicpm-guidance
#1
by anothercoder2 - opened
This comment has been hidden
Hi, thanks for your attention! The biggest difference is training data, and also there is a slight difference in the VIT implementation, which is the same as the difference between minicpm-v 2.0 and 2.5.