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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"]
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