vllm.transformers_utils.configs.nemotron_vl ¶

class Nemotron_Nano_VL_Config(PretrainedConfig):
    model_type = "Llama_Nemotron_Nano_VL"
    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,
        **kwargs,
    ):
        super().__init__(**kwargs)

        if vision_config is not None:
            assert (
                "auto_map" in vision_config
                and "AutoConfig" in vision_config["auto_map"]
            )
            vision_auto_config = get_class_from_dynamic_module(
                *vision_config["auto_map"]["AutoConfig"].split("--")[::-1]
            )
            self.vision_config = vision_auto_config(**vision_config)
        else:
            self.vision_config = PretrainedConfig()

        if llm_config is None:
            self.text_config = LlamaConfig()
        else:
            self.text_config = LlamaConfig(**llm_config)

        # 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