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# This file was automatically generated from src/transformers/models/qwen3_5/modular_qwen3_5.py.
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# Copyright 2025 The Qwen Team and The HuggingFace Inc. 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, layer_type_validation
from ...modeling_rope_utils import RopeParameters
[docs]
class Qwen3_5TextConfig(PreTrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3_5TextModel`]. It is used to instantiate a
Qwen3_5 model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of
Qwen3.5-9B-Instruct [Qwen/Qwen3.5-9B-Instruct](https://huggingface.co/Qwen/Qwen3.5-9B-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 248320):
Vocabulary size of the model. Defines the number of different tokens that can be represented by the
`inputs_ids`.
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 12288):
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 16):
Number of attention heads for each attention layer in the Transformer encoder.
num_key_value_heads (`int`, *optional*, defaults to 4):
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 checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
hidden_act (`str`, *optional*, defaults to `"silu"`):
The non-linear activation function in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 32768):
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_parameters (`RopeParameters`, *optional*):
Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
with longer `max_position_embeddings`.
attention_bias (`bool`, *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.
head_dim (`int`, *optional*, defaults to 256):
Projection weights dimension in multi-head attention.
linear_conv_kernel_dim (`int`, *optional*, defaults to 4):
Kernel size of the convolution used in linear attention layers.
linear_key_head_dim (`int`, *optional*, defaults to 128):
Dimension of each key head in linear attention.
linear_value_head_dim (`int`, *optional*, defaults to 128):
Dimension of each value head in linear attention.
linear_num_key_heads (`int`, *optional*, defaults to 16):
Number of key heads used in linear attention layers.
linear_num_value_heads (`int`, *optional*, defaults to 32):
Number of value heads used in linear attention layers.
layer_types (`list[str]`, *optional*):
Types of each layer (attention or linear).
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*):
Beginning of stream token id.
eos_token_id (`int`, *optional*):
End of stream token id.
```python
>>> from transformers import Qwen3_5TextModel, Qwen3_5TextConfig
>>> # Initializing a Qwen3.5 style configuration
>>> configuration = Qwen3_5TextConfig()
>>> # Initializing a model from the Qwen3.5-9B style configuration
>>> model = Qwen3_5TextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```
"""
model_type = "qwen3_5_text"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
base_config_key = "text_config"
def __init__(
self,
vocab_size=248320,
hidden_size=4096,
intermediate_size=12288,
num_hidden_layers=32,
num_attention_heads=16,
num_key_value_heads=4,
hidden_act="silu",
max_position_embeddings=32768,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
tie_word_embeddings=False,
rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
attention_bias=False,
attention_dropout=0.0,
head_dim=256,
linear_conv_kernel_dim=4,
linear_key_head_dim=128,
linear_value_head_dim=128,
linear_num_key_heads=16,
linear_num_value_heads=32,
layer_types=None,
pad_token_id: int | None = None,
bos_token_id: int | None = None,
eos_token_id: int | None = None,
**kwargs,
):
kwargs["ignore_keys_at_rope_validation"] = {"mrope_section", "mrope_interleaved"}
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.tie_word_embeddings = tie_word_embeddings
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
self.num_key_value_heads = num_key_value_heads
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.head_dim = head_dim
self.rope_parameters = rope_parameters
kwargs.setdefault("partial_rotary_factor", 0.25) # assign default for BC
self.layer_types = layer_types
if self.layer_types is None:
interval_pattern = kwargs.get("full_attention_interval", 4)
self.layer_types = [
"linear_attention" if bool((i + 1) % interval_pattern) else "full_attention"
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types, self.num_hidden_layers)
# linear attention part
self.linear_conv_kernel_dim = linear_conv_kernel_dim
self.linear_key_head_dim = linear_key_head_dim
self.linear_value_head_dim = linear_value_head_dim
self.linear_num_key_heads = linear_num_key_heads
self.linear_num_value_heads = linear_num_value_heads
super().__init__(**kwargs)
class Qwen3_5VisionConfig(PreTrainedConfig):
model_type = "qwen3_5"
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,
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
[docs]
class Qwen3_5Config(PreTrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3_5Model`]. It is used to instantiate a
Qwen3.5 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of
Qwen3.5-9B-Instruct [Qwen/Qwen3.5-9B-Instruct](https://huggingface.co/Qwen/Qwen3.5-9B-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 `Qwen3_5TextConfig`):
The config object or dictionary of the text backbone.
vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3_5VisionConfig`):
The config object or dictionary of the vision backbone.
image_token_id (`int`, *optional*, defaults to 248056):
The image token index to encode the image prompt.
video_token_id (`int`, *optional*, defaults to 248057):
The video token index to encode the image prompt.
vision_start_token_id (`int`, *optional*, defaults to 248053):
The start token index to encode the image prompt.
vision_end_token_id (`int`, *optional*, defaults to 248054):
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 Qwen3_5ForConditionalGeneration, Qwen3_5Config
>>> # Initializing a Qwen3.5 style configuration
>>> configuration = Qwen3_5Config()
>>> # Initializing a model from the Qwen3.5-9B style configuration
>>> model = Qwen3_5ForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen3_5"
sub_configs = {"vision_config": Qwen3_5VisionConfig, "text_config": Qwen3_5TextConfig}
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
text_config=None,
vision_config=None,
image_token_id=248056,
video_token_id=248057,
vision_start_token_id=248053,
vision_end_token_id=248054,
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
self.tie_word_embeddings = tie_word_embeddings
super().__init__(**kwargs)
__all__ = ["Qwen3_5Config", "Qwen3_5TextConfig"]