Any-to-Any
Transformers
Safetensors
minicpmo
feature-extraction
minicpm-o
minicpm-v
multimodal
full-duplex
custom_code
8-bit precision
Instructions to use Edith08/MiniCPM-o-4_5-q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Edith08/MiniCPM-o-4_5-q4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Edith08/MiniCPM-o-4_5-q4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Edith08/MiniCPM-o-4_5-q4: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Edith08/MiniCPM-o-4_5-q4/resolve/main/tokenizer.json
- Command line
-
hf download hf://Edith08/MiniCPM-o-4_5-q4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Edith08/MiniCPM-o-4_5-q4/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 376c2a2db7c385b095d9d9eef509cec27898e9d690a768fe75d162e69d0f18a7
- Size of remote file:
- 11.4 MB
- SHA256:
- 6d55eb34389b8c87403763cc1d80447d91c85c7cd39cd5e3c0dc2d49edad989d
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