from typing import Optional from transformers.configuration_utils import PretrainedConfig from transformers.modeling_rope_utils import rope_config_validation class PrismaVLVisionConfig(PretrainedConfig): model_type = "qwen3_vl" base_config_key = "vision_config" def __init__( self, depth=27, hidden_size=1152, hidden_act="gelu_pytorch_tanh", intermediate_size=4304, num_heads=16, in_channels=3, patch_size=16, spatial_merge_size=2, temporal_patch_size=2, out_hidden_size=3584, num_position_embeddings=2304, deepstack_visual_indexes=[8, 16, 24], initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.out_hidden_size = out_hidden_size self.num_position_embeddings = num_position_embeddings self.initializer_range = initializer_range self.deepstack_visual_indexes = deepstack_visual_indexes class PrismaVLTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PrismaVLTextModel`]. It is used to instantiate a Prisma-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Prisma-VL-4B-Instruct [Qwen/Prisma-VL-4B-Instruct](https://huggingface.co/Qwen/Prisma-VL-4B-Instruct). Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PreTrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 151936): Vocabulary size of the PrismaVL model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`PrismaVLModel`] hidden_size (`int`, *optional*, defaults to 4096): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 22016): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 32): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 32): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out [this paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `32`. head_dim (`int`, *optional*, defaults to 128): The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 128000): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 5000000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. Contains parameters for scaling RoPE to work with longer sequences. attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. ```python >>> from transformers import PrismaVLTextModel, PrismaVLTextConfig >>> # Initializing a PrismaVL style configuration >>> configuration = PrismaVLTextConfig() >>> # Initializing a model from the Prisma-VL-7B style configuration >>> model = PrismaVLTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen3_vl_text" base_config_key = "text_config" def __init__( self, vocab_size: Optional[int] = 151936, hidden_size: Optional[int] = 4096, intermediate_size: Optional[int] = 22016, num_hidden_layers: Optional[int] = 32, num_attention_heads: Optional[int] = 32, num_key_value_heads: Optional[int] = 32, head_dim: Optional[int] = 128, hidden_act: Optional[str] = "silu", max_position_embeddings: Optional[int] = 128000, initializer_range: Optional[float] = 0.02, rms_norm_eps: Optional[float] = 1e-6, use_cache: Optional[bool] = True, tie_word_embeddings: Optional[bool] = False, rope_theta: Optional[float] = 5000000.0, rope_scaling: Optional[dict] = None, attention_bias: Optional[bool] = False, attention_dropout: Optional[float] = 0.0, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.head_dim = head_dim self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.attention_bias = attention_bias self.attention_dropout = attention_dropout self.rope_theta = rope_theta self.rope_scaling = rope_scaling # Validate the correctness of rotary position embeddings parameters rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"}) super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class PrismaVLConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PrismaVLModel`]. It is used to instantiate a Prisma-VL model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Prisma-VL-4B-Instruct [Qwen/Prisma-VL-4B-Instruct](https://huggingface.co/Qwen/Prisma-VL-4B-Instruct). 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 `PrismaVLTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `PrismaVLVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151656): The video token index to encode the image prompt. vision_start_token_id (`int`, *optional*, defaults to 151652): The start token index to encode the image prompt. vision_end_token_id (`int`, *optional*, defaults to 151653): The end token index to encode the image prompt. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether to tie the word embeddings. ```python >>> from transformers import PrismaVLForConditionalGeneration, PrismaVLConfig >>> # Initializing a Prisma-VL style configuration >>> configuration = PrismaVLConfig() >>> # Initializing a model from the Prisma-VL-4B style configuration >>> model = PrismaVLForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "qwen3_vl" sub_configs = {"vision_config": PrismaVLVisionConfig, "text_config": PrismaVLTextConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=151655, video_token_id=151656, vision_start_token_id=151652, vision_end_token_id=151653, tie_word_embeddings=False, **kwargs, ): if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() if isinstance(text_config, dict): self.text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: self.text_config = self.sub_configs["text_config"]() self.image_token_id = image_token_id self.video_token_id = video_token_id self.vision_start_token_id = vision_start_token_id self.vision_end_token_id = vision_end_token_id super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings) __all__ = ["PrismaVLConfig", "PrismaVLTextConfig"]