Video-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
pedestrianqa
qwen2.5-vl
vision-language
video-question-answering
autonomous-driving
pedestrian-intention-prediction
pedestrian-trajectory-prediction
rationale-generation
text-generation-inference
Instructions to use namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct") model = AutoModelForMultimodalLM.from_pretrained("namansmishaps/PedestrianQA-TITAN-Qwen2.5-VL-3B-Instruct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df11ee434ceac0e70f7cda395c211b099dd0f6862c83a49a537bb07bfb28395f
- Size of remote file:
- 5 GB
- SHA256:
- 914d6c436718026f3f018cf961e85784978fea9ae6bd170cb2f910264492f148
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.