Any-to-Any
Transformers
Safetensors
English
diffusion
multimodal
unified-model
image-generation
image-editing
vision-language
Instructions to use internlm/InternLumina-U2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/InternLumina-U2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("internlm/InternLumina-U2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download nvidia/tokenizer.json from internlm/InternLumina-U2: direct link, hf CLI and curl.
- Browser
- Download file 12.2 MB
-
https://huggingface.co/internlm/InternLumina-U2/resolve/main/nvidia/tokenizer.json
- Command line
-
hf download hf://internlm/InternLumina-U2/nvidia/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/internlm/InternLumina-U2/resolve/main/nvidia/tokenizer.json
12.2 MB
- Xet hash:
- 7840b8e88402b98e720f5ec83e44e961682d928f7481a0fedc9c9ed122bad615
- Size of remote file:
- 12.2 MB
- SHA256:
- 1881cc2efdee659ac61c8a57ffcd64b8a591a8d4e557a9ace3be0707ec12a86c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.