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README.md

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+---
+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`](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) and is converted to the OpenVINO format. This model was obtained via the [nncf-quantization](https://huggingface.co/spaces/echarlaix/nncf-quantization) space with [optimum-intel](https://github.com/huggingface/optimum-intel).
+
+First make sure you have `optimum-intel` installed:
+
+```bash
+pip install optimum[openvino]
+```
+
+To load your model you can do as follows:
+
+```python
+from optimum.intel import OVModelForVisualCausalLM
+
+model_id = "echarlaix/SmolVLM2-500M-Video-Instruct-openvino-8bit-woq-data-free"
+model = OVModelForVisualCausalLM.from_pretrained(model_id)
+```