Video-Text-to-Text
Transformers
Safetensors
English
Chinese
videochat3
feature-extraction
video-language-model
vision-language-model
multimodal
video-understanding
image-understanding
streaming-video
custom_code
Instructions to use MCG-NJU/VideoChat3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MCG-NJU/VideoChat3-4B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MCG-NJU/VideoChat3-4B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,267 Bytes
62a321f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # coding=utf-8
# Copyright 2025 The VideoChat3 Team and 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.
"""
VideoChat3 model implementation for transformers library
"""
from configuration_videochat3 import VideoChat3Config, VideoChat3VisionConfig
from modeling_videochat3 import VideoChat3ForConditionalGeneration, VideoChat3MultiModalProjector, VideoChat3VisionModel
from processing_videochat3 import VideoChat3Processor
from video_processing_videochat3 import VideoChat3VideoProcessor
__all__ = [
"VideoChat3Config",
"VideoChat3VisionConfig",
"VideoChat3ForConditionalGeneration",
"VideoChat3VisionModel",
"VideoChat3MultiModalProjector",
"VideoChat3Processor",
"VideoChat3VideoProcessor",
]
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