Instructions to use lmms-lab/LLaVA-NeXT-Video-32B-Qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmms-lab/LLaVA-NeXT-Video-32B-Qwen with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("lmms-lab/LLaVA-NeXT-Video-32B-Qwen") model = AutoModelForCausalLM.from_pretrained("lmms-lab/LLaVA-NeXT-Video-32B-Qwen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from lmms-lab/LLaVA-NeXT-Video-32B-Qwen: direct link, hf CLI and curl.
- Browser
- Download file 7.86 kB
-
https://huggingface.co/lmms-lab/LLaVA-NeXT-Video-32B-Qwen/resolve/main/training_args.bin
- Command line
-
hf download hf://lmms-lab/LLaVA-NeXT-Video-32B-Qwen/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/lmms-lab/LLaVA-NeXT-Video-32B-Qwen/resolve/main/training_args.bin
7.86 kB
- Xet hash:
- 429a79ed05b97b7ac8f9095cdcb8e8ec4cd2eac51dfa6a860b1fb7204e65789d
- Size of remote file:
- 7.86 kB
- SHA256:
- b961d71ce1fcfb58c4bffb7490d420e976d5f5dfd191d11318ca0512811275ff
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.