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<advanced_parameters value="{'statistics_path': None, 'lora_adapter_rank': 256, 'group_size_fallback_mode': 'error', 'min_adjusted_group_size': 32, '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}, 'backend_params': {}, 'codebook': None, 'adaptive_codebook_params': {'value_type': 'f8e4m3', 'across_blocks': False, 'num_elements': 16}}" />
<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>
<diffusers_version value="0.37.1" />
<nncf_version value="3.4.0" />
<optimum_intel_version value="2.2.0" />
<optimum_version value="2.3.0" />
<pytorch_version value="2.14.0" />
<transformers_version value="4.57.6" />
</optimum>
<runtime_options>
<ACTIVATIONS_SCALE_FACTOR value="8.0" />
</runtime_options>
</rt_info>
</net>