Visual Question Answering
Transformers
Safetensors
English
Chinese
minicpmv
feature-extraction
custom_code
Eval Results
Instructions to use openbmb/MiniCPM-V-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openbmb/MiniCPM-V-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="openbmb/MiniCPM-V-2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("openbmb/MiniCPM-V-2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
GGUF file
#6
by BB8-dev - opened
Thank you for developing such an excellent language model. Could I ask if there's any chance to get the GGUF file of this model?
We are working on this, please stay tuned!
Created a PR: https://github.com/ggerganov/llama.cpp/pull/6919.
I created a folder called "minicpmv" in the examples folder of llama.cpp.
More detail can be seen in llama.cpp/examples/minicpmv/README.md