Video-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
text-generation-inference
Instructions to use Video-R1/Video-R1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Video-R1/Video-R1-7B with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Video-R1/Video-R1-7B") model = AutoModelForMultimodalLM.from_pretrained("Video-R1/Video-R1-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download rng_state_1.pth from Video-R1/Video-R1-7B: direct link, hf CLI and curl.
- Browser
- Download file 15.3 kB
-
https://huggingface.co/Video-R1/Video-R1-7B/resolve/refs%2Fpr%2F1/rng_state_1.pth
- Command line
-
hf download hf://Video-R1/Video-R1-7B@refs/pr/1/rng_state_1.pth
-
curl -L -o rng_state_1.pth https://huggingface.co/Video-R1/Video-R1-7B/resolve/refs%2Fpr%2F1/rng_state_1.pth
15.3 kB
- Xet hash:
- 1329689dcc7762316ad179c4429a6f12983eb40320e152df7487f3115a189000
- Size of remote file:
- 15.3 kB
- SHA256:
- f715ce8bec4434bb7dba02d35eb88384cb19aed012c4d64eee0b8139df39710c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.