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@@ -129,7 +129,7 @@ q_model.save_pretrained("smolvlm_static_int8")
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  or quivalently using the CLI:
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  ```bash
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- optimum-cli export openvino -m HuggingFaceTB/SmolVLM2-256M-Video-Instruct --weight-format int8 --dataset contextual --num-samples 50 smolvlm_static_int8/
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  ```
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  Quantizing activations adds small errors that can build up and affect accuracy, so careful testing afterward is important. More information and examples can be found in [our documentation](https://huggingface.co/docs/optimum-intel/en/openvino/optimization#pipeline-quantization).
 
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  or quivalently using the CLI:
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  ```bash
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+ optimum-cli export openvino -m HuggingFaceTB/SmolVLM2-256M-Video-Instruct --quant-mode int8 --dataset contextual --num-samples 50 smolvlm_static_int8/
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  ```
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  Quantizing activations adds small errors that can build up and affect accuracy, so careful testing afterward is important. More information and examples can be found in [our documentation](https://huggingface.co/docs/optimum-intel/en/openvino/optimization#pipeline-quantization).