SmolVLM2-500M-Video-Instruct-openvino-8bit-static / openvino_text_embeddings_model.xml
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<?xml version="1.0"?>
<net name="Model199" version="11">
<layers>
<layer id="0" name="input" type="Parameter" version="opset1">
<data shape="?,?" element_type="i64" />
<output>
<port id="0" precision="I64" names="input">
<dim>-1</dim>
<dim>-1</dim>
</port>
</output>
</layer>
<layer id="1" name="self.weight" type="Const" version="opset1">
<data element_type="u8" shape="49280, 960" offset="0" size="47308800" />
<output>
<port id="0" precision="U8">
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<dim>960</dim>
</port>
</output>
</layer>
<layer id="2" name="Convert_834939" type="Convert" version="opset1">
<data destination_type="f16" />
<input>
<port id="0" precision="U8">
<dim>49280</dim>
<dim>960</dim>
</port>
</input>
<output>
<port id="1" precision="FP16">
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<dim>960</dim>
</port>
</output>
</layer>
<layer id="3" name="self.weight/zero_point" type="Const" version="opset1">
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<output>
<port id="0" precision="U8">
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<layer id="4" name="Convert_834942" type="Convert" version="opset1">
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<port id="0" precision="U8">
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</port>
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<port id="1" precision="FP16">
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<dim>1</dim>
</port>
</output>
</layer>
<layer id="5" name="self.weight/zero_point/subtract" type="Subtract" version="opset1">
<data auto_broadcast="numpy" />
<input>
<port id="0" precision="FP16">
<dim>49280</dim>
<dim>960</dim>
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<port id="1" precision="FP16">
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</input>
<output>
<port id="2" precision="FP16">
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<dim>960</dim>
</port>
</output>
</layer>
<layer id="6" name="self.weight/scale" type="Const" version="opset1">
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<output>
<port id="0" precision="FP16">
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</layer>
<layer id="7" name="self.weight/fq_weights_0" type="Multiply" version="opset1">
<data auto_broadcast="numpy" />
<input>
<port id="0" precision="FP16">
<dim>49280</dim>
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<port id="1" precision="FP16">
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</layer>
<layer id="8" name="self.weight/fq_weights_0/convert" type="Convert" version="opset1">
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<port id="0" precision="FP16">
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<output>
<port id="1" precision="FP32">
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<dim>960</dim>
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<layer id="9" name="aten::embedding/Convert" type="Convert" version="opset1">
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<layer id="10" name="aten::embedding/Constant" type="Const" version="opset1">
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<output>
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<layer id="11" name="aten::embedding/Gather" type="Gather" version="opset8">
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<output>
<port id="3" precision="FP32" names="inputs_embeds">
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</output>
</layer>
<layer id="12" name="Result_526194" type="Result" version="opset1" output_names="inputs_embeds">
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</port>
</input>
</layer>
</layers>
<edges>
<edge from-layer="0" from-port="0" to-layer="9" to-port="0" />
<edge from-layer="1" from-port="0" to-layer="2" to-port="0" />
<edge from-layer="2" from-port="1" to-layer="5" to-port="0" />
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<edge from-layer="11" from-port="3" to-layer="12" to-port="0" />
</edges>
<rt_info>
<Runtime_version value="2025.2.0-19140-c01cd93e24d-releases/2025/2" />
<conversion_parameters>
<framework value="pytorch" />
<is_python_object value="True" />
</conversion_parameters>
<nncf>
<friendly_names_were_updated value="True" />
<version value="2.17.0" />
<weight_compression>
<advanced_parameters value="{'statistics_path': None, 'awq_params': {'subset_size': 32, 'percent_to_apply': 0.002, 'alpha_min': 0.0, 'alpha_max': 1.0, 'steps': 100, 'prefer_data_aware_scaling': True}, 'scale_estimation_params': {'subset_size': 64, 'initial_steps': 5, 'scale_steps': 5, 'weight_penalty': -1.0}, 'gptq_params': {'damp_percent': 0.1, 'block_size': 128, 'subset_size': 128}, 'lora_correction_params': {'adapter_rank': 8, 'num_iterations': 3, 'apply_regularization': True, 'subset_size': 128, 'use_int8_adapters': True}, 'lora_adapter_rank': 256, 'backend_params': {}}" />
<all_layers value="False" />
<awq value="False" />
<backup_mode value="int8_asym" />
<compression_format value="dequantize" />
<gptq value="False" />
<group_size value="-1" />
<ignored_scope value="[]" />
<lora_correction value="False" />
<mode value="int8_asym" />
<ratio value="1.0" />
<scale_estimation value="False" />
<sensitivity_metric value="weight_quantization_error" />
</weight_compression>
</nncf>
<optimum>
<nncf_version value="2.17.0" />
<optimum_intel_version value="1.24.0" />
<optimum_version value="1.26.1" />
<pytorch_version value="2.7.1" />
<transformers_version value="4.52.4" />
</optimum>
</rt_info>
</net>