# -------------------------------------------------------- # Adapted from https://huggingface.co/OpenGVLab/InternVL2-Llama3-76B under MIT License # LICENSE is in incl_licenses directory. # -------------------------------------------------------- from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging from .configuration_nemotron_h import NemotronHConfig from .configuration_radio import RADIOConfig logger = logging.get_logger(__name__) class NemotronH_Nano_VL_V2_Config(PretrainedConfig): model_type = 'NemotronH_Nano_VL_V2' is_composition = True def __init__( self, vision_config=None, llm_config=None, force_image_size=None, downsample_ratio=0.5, template=None, ps_version='v1', image_tag_type="internvl", projector_hidden_size=4096, vit_hidden_size=1280, attn_implementation="flash_attention_2", video_pruning_rate: float = 0.0, **kwargs ): super().__init__(**kwargs) if vision_config is not None: self.vision_config = RADIOConfig(**vision_config) else: self.vision_config = RADIOConfig() # Handle both cases: when loading from JSON (llm_config is dict) and when called internally by transformers (llm_config is None) if llm_config is not None: self.llm_config = NemotronHConfig(**llm_config) else: self.llm_config = NemotronHConfig() # Assign configuration values self.force_image_size = force_image_size self.downsample_ratio = downsample_ratio self.template = template # TODO move out of here and into the tokenizer self.ps_version = ps_version # Pixel shuffle version self.image_tag_type = image_tag_type # TODO: into the tokenizer too? self.projector_hidden_size = projector_hidden_size self.vit_hidden_size = vit_hidden_size self.video_pruning_rate = video_pruning_rate self._attn_implementation = attn_implementation self.vision_config.use_flash_attn = self._attn_implementation is not None and "flash_attention" in self._attn_implementation self.llm_config._attn_implementation = self._attn_implementation