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# coding=utf-8
# Copyright 2024 The Qwen team, Alibaba Group 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.
"""RWKV079Qwen3 model configuration"""
#Never gonna give you up
from typing import Optional
from transformers.configuration_utils import PretrainedConfig#, layer_type_validation
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
# class RWKV07BMoEConfig(PretrainedConfig):
# r"""
# This is the configuration class to store the configuration of a [`RWKV07BMoEModel`]. It is used to instantiate a
# RWKV079Qwen3 model according to the specified arguments, defining the model architecture. Instantiating a configuration
# with the defaults will yield a similar configuration to that of
# Qwen3-7B-beta [Qwen/Qwen3-7B-beta](https://huggingface.co/Qwen/Qwen3-7B-beta).
# 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 RWKV079Qwen3 model. Defines the number of different tokens that can be represented by the
# `inputs_ids` passed when calling [`RWKV07BMoEModel`]
# 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 checkout [this
# paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `32`.
# lora_rank_decay (`int`, *optional*):
# The rank of the lora used to generate decay.
# lora_rank_iclr (`int`, *optional*):
# The rank of the lora used to generate the in-context learning rate.
# lora_rank_value_residual_mix (`int`, *optional*):
# The rank of the lora used to generate the value residual mix amount.
# lora_rank_value_gate (`int`, *optional*):
# The rank of the lora used to generate the gate.
# 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 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_theta (`float`, *optional*, defaults to 10000.0):
# The base period of the RoPE embeddings.
# rope_scaling (`Dict`, *optional*):
# Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
# and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
# accordingly.
# Expected contents:
# `rope_type` (`str`):
# The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
# 'llama3'], with 'default' being the original RoPE implementation.
# `factor` (`float`, *optional*):
# Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
# most scaling types, a `factor` of x will enable the model to handle sequences of length x *
# original maximum pre-trained length.
# `original_max_position_embeddings` (`int`, *optional*):
# Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
# pretraining.
# `attention_factor` (`float`, *optional*):
# Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
# computation. If unspecified, it defaults to value recommended by the implementation, using the
# `factor` field to infer the suggested value.
# `beta_fast` (`float`, *optional*):
# Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
# ramp function. If unspecified, it defaults to 32.
# `beta_slow` (`float`, *optional*):
# Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
# ramp function. If unspecified, it defaults to 1.
# `short_factor` (`List[float]`, *optional*):
# Only used with 'longrope'. The scaling factor to be applied to short contexts (<
# `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
# size divided by the number of attention heads divided by 2
# `long_factor` (`List[float]`, *optional*):
# Only used with 'longrope'. The scaling factor to be applied to long contexts (<
# `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
# size divided by the number of attention heads divided by 2
# `low_freq_factor` (`float`, *optional*):
# Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
# `high_freq_factor` (`float`, *optional*):
# Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
# use_sliding_window (`bool`, *optional*, defaults to `False`):
# Whether to use sliding window attention.
# sliding_window (`int`, *optional*, defaults to 4096):
# Sliding window attention (SWA) window size. If not specified, will default to `4096`.
# max_window_layers (`int`, *optional*, defaults to 28):
# The number of layers that use SWA (Sliding Window Attention). The bottom layers use SWA while the top use full attention.
# attention_dropout (`float`, *optional*, defaults to 0.0):
# The dropout ratio for the attention probabilities.
# ```python
# >>> from transformers import RWKV07BMoEModel, RWKV079Qwen3Config
# >>> # Initializing a RWKV079Qwen3 style configuration
# >>> configuration = RWKV079Qwen3Config()
# >>> # Initializing a model from the RWKV079Qwen3-7B style configuration
# >>> model = RWKV07BMoEModel(configuration)
# >>> # Accessing the model configuration
# >>> configuration = model.config
# ```"""
# model_type = "rwkv07b_moe"
# keys_to_ignore_at_inference = ["past_key_values"]
# # Default tensor parallel plan for base model `Qwen3Moe`
# base_model_tp_plan = {
# #NoPE-GQA
# "layers.*.self_attn.q_proj": "colwise",
# "layers.*.self_attn.k_proj": "colwise",
# "layers.*.self_attn.v_proj": "colwise",
# "layers.*.self_attn.o_proj": "rowwise",
# #RoPE-RWKV
# "layers.*.self_attn.receptance": "colwise",
# "layers.*.self_attn.key": "colwise",
# "layers.*.self_attn.value": "colwise",
# "layers.*.self_attn.output": "rowwise",
# "layers.*.mlp.experts.*.gate_proj": "colwise",
# "layers.*.mlp.experts.*.up_proj": "colwise",
# "layers.*.mlp.experts.*.down_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"]),
# }
# def __init__(
# self,
# vocab_size=151936,
# hidden_size=4096,
# intermediate_size=22016,
# num_hidden_layers=32,
# num_attention_heads=32,
# num_key_value_heads=32,
# lora_rank_tokenshift=None,
# lora_rank_decay=None,
# lora_rank_iclr=None,
# lora_rank_value_residual_mix=None,
# lora_rank_value_key_mix=None,
# lora_rank_gate=None,
# hidden_act="silu",
# max_position_embeddings=32768,
# initializer_range=0.02,
# rms_norm_eps=1e-6,
# use_cache=True,
# tie_word_embeddings=False,
# use_rope=True,
# rope_theta=10000.0,
# rope_scaling=None,
# use_sliding_window=False,
# sliding_window=4096,
# max_window_layers=28,
# first_attention_layer=9999,
# first_post_attention_layer=9999,
# attention_striping=1,
# last_striping_layer=99999,
# layer_types=None,
# attention_dropout=0.0,
# attention_bias=True,
# attention_output_bias=False,
# gate_rank_type=2,
# balance_state=True,
# groupnorm_att=False,
# use_tokenshift=False,
# decoder_sparse_step=1,
# moe_intermediate_size=768,
# num_experts_per_tok=8,
# num_experts=128,
# norm_topk_prob=False,
# output_router_logits=False,
# router_aux_loss_coef=0.001,
# mlp_only_layers=None,
# **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
# self.use_sliding_window = use_sliding_window
# self.sliding_window = sliding_window if use_sliding_window else None
# self.max_window_layers = max_window_layers
# self.first_attention_layer = first_attention_layer
# self.first_post_attention_layer = first_post_attention_layer
# self.attention_striping = attention_striping
# self.last_striping_layer = last_striping_layer
# # 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.lora_rank_tokenshift = lora_rank_tokenshift
# self.lora_rank_decay = lora_rank_decay
# self.lora_rank_iclr = lora_rank_iclr
# self.lora_rank_value_residual_mix = lora_rank_value_residual_mix
# self.lora_rank_gate = lora_rank_gate
# self.hidden_act = hidden_act
# self.initializer_range = initializer_range
# self.rms_norm_eps = rms_norm_eps
# self.use_cache = use_cache
# self.use_rope = use_rope
# self.rope_theta = rope_theta
# self.rope_scaling = rope_scaling
# self.attention_dropout = attention_dropout
# # Validate the correctness of rotary position embeddings parameters
# # BC: if there is a 'type' field, move it to 'rope_type'.
# if self.rope_scaling is not None and "type" in self.rope_scaling:
# self.rope_scaling["rope_type"] = self.rope_scaling["type"]
# self.rope_parameters = rope_scaling
# #rope_config_validation(self)
# self.layer_types = layer_types
# if self.layer_types is None:
# self.layer_types = [
# "sliding_attention"
# if self.sliding_window is not None and i >= self.max_window_layers
# else "full_attention"
# for i in range(self.num_hidden_layers)
# ]
# #layer_type_validation(self.layer_types)
# self.attention_bias = attention_bias
# self.attention_output_bias = attention_output_bias
# self.gate_rank_type = gate_rank_type
# self.balance_state = balance_state
# self.groupnorm_att = groupnorm_att
# self.use_tokenshift = use_tokenshift
# # MoE arguments
# self.decoder_sparse_step = decoder_sparse_step
# self.moe_intermediate_size = moe_intermediate_size
# self.num_experts_per_tok = num_experts_per_tok
# self.num_experts = num_experts
# self.norm_topk_prob = norm_topk_prob
# self.output_router_logits = output_router_logits
# self.router_aux_loss_coef = router_aux_loss_coef
# self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
# super().__init__(
# tie_word_embeddings=tie_word_embeddings,
# **kwargs,
# )
from typing import Optional, TypedDict
class RopeParameters(TypedDict):
"""
Args:
rope_theta (`float`):
The base period of the RoPE embeddings.
rope_type (`str`, *optional*, defaults to "default"):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
'llama3'], with 'default' being the original RoPE implementation.
factor (`float`, *optional*):
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
original maximum pre-trained length.
original_max_position_embeddings (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
attention_factor (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested value.
beta_fast (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
ramp function. If unspecified, it defaults to 32.
beta_slow (`float`, *optional*):
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
ramp function. If unspecified, it defaults to 1.
short_factor (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
long_factor (`list[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
low_freq_factor (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
high_freq_factor (`float`, *optional*):
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
"""
rope_theta: float
rope_type: Optional[str]
factor: Optional[float]
original_max_position_embeddings: Optional[int]
attention_factor: Optional[float]
beta_fast: Optional[float]
beta_slow: Optional[float]
short_factor: Optional[list[float]]
long_factor: Optional[list[float]]
low_freq_factor: Optional[float]
high_freq_factor: Optional[float]
class Qwen3VLMoeTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3VLMoeTextModel`]. It is used to instantiate a
Qwen3-VL-MOE 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-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-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 Qwen2MoE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Qwen2MoeModel`]
hidden_size (`int`, *optional*, defaults to 2048):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 5632):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 24):
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 16):
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` 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.
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.
decoder_sparse_step (`int`, *optional*, defaults to 1):
The frequency of the MoE layer.
moe_intermediate_size (`int`, *optional*, defaults to 1408):
Intermediate size of the routed expert.
num_experts_per_tok (`int`, *optional*, defaults to 4):
Number of selected experts.
num_experts (`int`, *optional*, defaults to 60):
Number of routed experts.
mlp_only_layers (`List[int]`, *optional*, defaults to `[]`):
Indicate which layers use Qwen3VLMoeMLP rather than Qwen3VLMoeSparseMoeBlock
The list contains layer index, from 0 to num_layers-1 if we have num_layers layers
If `mlp_only_layers` is empty, `decoder_sparse_step` is used to determine the sparsity.
rope_parameters (`RopeParameters`, *optional*):
Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty 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`.
head_dim (`int`, *optional*):
The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.
```python
>>> from transformers import Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig
>>> # Initializing a Qwen3VLMoe style configuration
>>> configuration = Qwen3VLMoeConfig()
>>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration
>>> model = Qwen3VLMoeForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "qwen3_vl_moe_text"
base_config_key = "text_config"
keys_to_ignore_at_inference = ["past_key_values"]
# Default tensor parallel plan for base model `Qwen3VLMoe`
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"]),
}
def __init__(
self,
vocab_size: Optional[int] = 151936,
hidden_size: Optional[int] = 2048,
intermediate_size: Optional[int] = 5632,
num_hidden_layers: Optional[int] = 24,
num_attention_heads: Optional[int] = 16,
num_key_value_heads: Optional[int] = 16,
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,
attention_bias: Optional[bool] = False,
attention_dropout: Optional[float] = 0.0,
decoder_sparse_step: Optional[int] = 1,
moe_intermediate_size: Optional[int] = 1408,
num_experts_per_tok: Optional[int] = 4,
num_experts: Optional[int] = 60,
mlp_only_layers: Optional[list[int]] = None,
rope_parameters: Optional[RopeParameters] = None,
head_dim: Optional[int] = None,
**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
self.layer_types = None
# 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.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 or hidden_size // num_attention_heads
# Try to set `rope_scaling` if available, otherwise use `rope_parameters`
rope_scaling = kwargs.pop("rope_scaling", None)
self.rope_parameters = rope_scaling or rope_parameters
self.sliding_window = None
self.max_window_layers = 0
if self.layer_types is None:
self.layer_types = [
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
for i in range(self.num_hidden_layers)
]
# Validate the correctness of rotary position embeddings parameters
rope_theta = kwargs.get("rope_theta", 5000000.0)
# standardize_rope_params(self, rope_theta=rope_theta)
rope_config_validation(self, ignore_keys={"mrope_section", "mrope_interleaved"})
# MoE arguments
self.decoder_sparse_step = decoder_sparse_step
self.moe_intermediate_size = moe_intermediate_size
self.num_experts_per_tok = num_experts_per_tok
self.num_experts = num_experts
self.mlp_only_layers = [] if mlp_only_layers is None else mlp_only_layers
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class Qwen3VLMoeVisionConfig(PretrainedConfig):
model_type = "qwen3_vl_moe"
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 RWKV07BMoEConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Qwen3VLMoeModel`]. It is used to instantiate a
Qwen3-VL-MOE 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-VL-30B-A3B-Instruct [Qwen/Qwen3-VL-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-30B-A3B-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 `Qwen3VLMoeTextConfig`):
The config object or dictionary of the text backbone.
vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Qwen3VLMoeVisionConfig`):
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 Qwen3VLMoeForConditionalGeneration, Qwen3VLMoeConfig
>>> # Initializing a Qwen3-VL-MOE style configuration
>>> configuration = Qwen3VLMoeConfig()
>>> # Initializing a model from the Qwen3-VL-30B-A3B style configuration
>>> model = Qwen3VLMoeForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "rwkv07b_moe"
sub_configs = {"vision_config": Qwen3VLMoeVisionConfig, "text_config": Qwen3VLMoeTextConfig}
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__ = ["Qwen3VLMoeConfig", "Qwen3VLMoeTextConfig"] |