Task
Image Text To Text
S
HuggingFaceTB/SmolVLM2-500M-Video-Instruct
Image Text To Text·HuggingFaceTB· 670.7K· 155
transformers
apache-2.0
507.5M params
dataset:HuggingFaceM4/the_cauldrondataset:HuggingFaceM4/Docmatixdataset:lmms-lab/LLaVA-OneVision-Datadataset:lmms-lab/M4-Instruct-Datadataset:HuggingFaceFV/finevideo
SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex mult
# pull & run locally
pip install mlforge-sdk && mlforge pull HuggingFaceTB/SmolVLM2-500M-Video-Instruct
pip install mlforge-sdk && mlforge pull HuggingFaceTB/SmolVLM2-500M-Video-Instruct
Model details
Provider
HuggingFaceTB
Framework
transformers
Parameters
507.5M
Size
9.4 GB
License
apache-2.0
Downloads
670.7K
Likes
155
Paper
arXiv:2504.05299
Updated
2025-04-08
About HuggingFaceTB/SmolVLM2-500M-Video-Instruct
SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited. Model Summary