Video-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
video-understanding
reasoning
multimodal
reinforcement-learning
question-answering
text-generation-inference
Instructions to use Falconss1/VideoThinker-R1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconss1/VideoThinker-R1-3B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Falconss1/VideoThinker-R1-3B") model = AutoModelForMultimodalLM.from_pretrained("Falconss1/VideoThinker-R1-3B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Falconss1/VideoThinker-R1-3B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/Falconss1/VideoThinker-R1-3B/resolve/main/tokenizer.json
- Command line
-
hf download hf://Falconss1/VideoThinker-R1-3B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Falconss1/VideoThinker-R1-3B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- ff206f79d3c0e627de7956b4ef37c2b8eeab5e83490ff04516bc674e4c1d6e77
- Size of remote file:
- 11.4 MB
- SHA256:
- 5eee858c5123a4279c3e1f7b81247343f356ac767940b2692a928ad929543214
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.