Any-to-Any
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
custom_code
text-generation-inference
Instructions to use modelscope/Nexus-GenV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modelscope/Nexus-GenV2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("modelscope/Nexus-GenV2", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("modelscope/Nexus-GenV2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_decoder.bin from modelscope/Nexus-GenV2: direct link, hf CLI and curl.
- Browser
- Download file 23.9 GB
-
https://huggingface.co/modelscope/Nexus-GenV2/resolve/main/generation_decoder.bin
- Command line
-
hf download hf://modelscope/Nexus-GenV2/generation_decoder.bin
-
curl -L -o generation_decoder.bin https://huggingface.co/modelscope/Nexus-GenV2/resolve/main/generation_decoder.bin
23.9 GB
- Xet hash:
- db67555def71d99e724c7e2024c0244384497f91cbeef2c85758149433538c50
- Size of remote file:
- 23.9 GB
- SHA256:
- 77a225a1792de87c262146d6ff702bafd23834f10d4140ea79ee61e923337fa2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.