riaz.somc/transformers
1
0
Fork 0
mirror of https://github.com/huggingface/transformers.git synced 2025-08-03 03:31:05 +06:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

fixed docstring in modular_qwen2_5_vl.py (#38798)

Browse Source 
* fixed docstring in modular_qwen2_5_vl.py

* Regenerate file to match docstring update
This commit is contained in:
Lawrence Feng 2025-06-13 11:09:51 -07:00 committed by GitHub
parent 9bec2654ed
commit 8b73799500
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
2 changed files with 8 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
View File

@ -1307,8 +1307,8 @@ class Qwen2_5_VLModel(Qwen2_5_VLPreTrainedModel):
r"""
pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
The tensors corresponding to the input videos. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2_5_VLImageProcessor`] for processing videos.
[`AutoImageProcessor`]. See [`Qwen2VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2VLImageProcessor`] for processing videos.
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
@ -1549,8 +1549,8 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2_5_VLPreTrainedModel, GenerationMi
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
The tensors corresponding to the input videos. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2_5_VLImageProcessor`] for processing videos.
[`AutoImageProcessor`]. See [`Qwen2VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2VLImageProcessor`] for processing videos.
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):

8
src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
View File

@ -628,8 +628,8 @@ class Qwen2_5_VLModel(Qwen2VLModel):
r"""
pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
The tensors corresponding to the input videos. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2_5_VLImageProcessor`] for processing videos.
[`AutoImageProcessor`]. See [`Qwen2VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2VLImageProcessor`] for processing videos.
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
@ -786,8 +786,8 @@ class Qwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
pixel_values_videos (`torch.FloatTensor` of shape `(seq_length, num_channels * temporal_size * image_size * image_size)):
The tensors corresponding to the input videos. Pixel values can be obtained using
[`AutoImageProcessor`]. See [`Qwen2_5_VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2_5_VLImageProcessor`] for processing videos.
[`AutoImageProcessor`]. See [`Qwen2VLImageProcessor.__call__`] for details. [`Qwen2_5_VLProcessor`] uses
[`Qwen2VLImageProcessor`] for processing videos.
image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):