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-JAAD-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-JAAD-Qwen2.5-VL-3B-Instruct with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("namansmishaps/PedestrianQA-JAAD-Qwen2.5-VL-3B-Instruct") model = AutoModelForMultimodalLM.from_pretrained("namansmishaps/PedestrianQA-JAAD-Qwen2.5-VL-3B-Instruct") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f4464ccf676bb9ddcc4e79b08ade062f7f7b1c1932ecf2fa66e7ee5afe01b470
- Size of remote file:
- 14.5 kB
- SHA256:
- 5760f1347dfb235ea1535097ed60f32852e195369a850d128196e21060244ece
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