metadata
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/M4-Instruct-Data
- HuggingFaceFV/finevideo
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
- lmms-lab/LLaVA-Video-178K
- orrzohar/Video-STaR
- Mutonix/Vript
- TIGER-Lab/VISTA-400K
- Enxin/MovieChat-1K_train
- ShareGPT4Video/ShareGPT4Video
pipeline_tag: image-text-to-text
language:
- en
base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
tags:
- openvino
- nncf
- 8-bit
This model is a quantized version of HuggingFaceTB/SmolVLM2-500M-Video-Instruct and is converted to the OpenVINO format. This model was obtained via the nncf-quantization space with optimum-intel.
First make sure you have optimum-intel installed:
pip install optimum[openvino]
To load your model you can do as follows:
from optimum.intel import OVModelForVisualCausalLM
model_id = "echarlaix/SmolVLM2-500M-Video-Instruct-openvino-8bit-woq-data-free"
model = OVModelForVisualCausalLM.from_pretrained(model_id)