# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/glm46v/modular_glm46v.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_glm46v.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 the HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...configuration_utils import PreTrainedConfig from ..auto import CONFIG_MAPPING, AutoConfig class Glm46VConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`Glm4vModel`]. It is used to instantiate a GLM-4.6V model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of GLM-4.1V-9B-Thinking [zai-org/GLM-4.1V-9B-Thinking](https://huggingface.co/zai-org/GLM-4.1V-9B-Thinking). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Glm4vVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151343): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151344): The video token index to encode the image prompt. image_start_token_id (`int`, *optional*, defaults to 151339): The image start token index to encode the start of image. image_end_token_id (`int`, *optional*, defaults to 151340): The image end token index to encode the end of image. video_start_token_id (`int`, *optional*, defaults to 151361): The video start token index to encode the start of video. video_end_token_id (`int`, *optional*, defaults to 151362): The video end token index to encode the end of video. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie weight embeddings ```python >>> from transformers import Glm46VForConditionalGeneration, Glm46VConfig >>> # Initializing a GLM-4.6V style configuration >>> configuration = Glm46VConfig() >>> # Initializing a model from the GLM-4.6V style configuration >>> model = Glm4vForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "glm46v" sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=151343, video_token_id=151344, image_start_token_id=151339, image_end_token_id=151340, video_start_token_id=151361, video_end_token_id=151362, tie_word_embeddings=False, **kwargs, ): if isinstance(vision_config, dict): vision_config["model_type"] = vision_config.get("model_type", "glm4v_vision") self.vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config) elif vision_config is None: self.vision_config = CONFIG_MAPPING["glm4v_vision"]() if isinstance(text_config, dict): text_config["model_type"] = text_config.get("model_type", "glm4v_text") self.text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config) elif text_config is None: self.text_config = CONFIG_MAPPING["glm4v_text"]() self.image_token_id = image_token_id self.video_token_id = video_token_id self.video_start_token_id = video_start_token_id self.video_end_token_id = video_end_token_id self.image_start_token_id = image_start_token_id self.image_end_token_id = image_end_token_id self.tie_word_embeddings = tie_word_embeddings super().__init__(**kwargs) __all__ = ["Glm46VConfig"]