BAGEL - Open source multimodal base model launched by Wordpress

What's BAGEL?

BAGEL is a multimodal base model open-sourced by ByteDance with 14 billion parameters, of which 7 billion are active. The model is based on the Mixed Transformer Expert Architecture (MoT), which captures pixel-level and semantic-level features of an image with two independent encoders, and supports efficient processing of image, text, video, and other multimodal data.BAGEL supports text-to-image generation, image editing, video frame prediction, and other functions, and the model performance outperforms several top open-source models in multimodal comprehension benchmarks, such as Qwen2.5 -BAGEL is pre-trained on massive multimodal labeled data, covering language, image, video, and network data, and can learn a wide range of multimodal features and patterns. The models are suitable for scenarios such as content creation, 3D scene generation and user interaction experience, providing powerful technical support for multimodal applications.

Main functions of BAGEL

• Image and Text Fusion Understanding: Understanding the relationship between images and text for accurate matching.
• Video Content Understanding: Analyzing dynamic information and semantic content in videos.
• Text-to-Image Generation: Generate high-quality images based on text descriptions.
• Image editing and modification: Free-form editing of existing images.
• Video Frame Prediction: Predicts future frames of a video based on previous frames.
• 3D scene understanding and manipulation: Recognize and manipulate three-dimensional objects.
• World Navigation: Path planning and navigation in a 3D environment.
• cross-modal search: Retrieve images or videos based on text.
• Multimodal fusion task: Fusion of data from different modalities to generate synthesized results.

BAGEL's official website address

• Project website::https://bagel-ai.org/
• Github repository::https://github.com/bytedance-seed/BAGEL
• HuggingFace Model Library::https://huggingface.co/ByteDance-Seed/BAGEL
• Technical Papers::https://arxiv.org/pdf/2505.14683
• Experience Dem Online::https://demo.bagel-ai.org/

How to use BAGEL

• Hugging Face Model Library Access::
  • Installation of dependencies::

pip install transformers

• • Loading Models::

from transformers import AutoModel, AutoTokenizer

model_name = "ByteDance-Seed/BAGEL-7B-MoT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

• • Using the model::

text = "生成一个日落的图像"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

• GitHub Repository Access::
  • clone warehouse::

git clone https://github.com/bytedance-seed/BAGEL.git
cd BAGEL

• • Installation of dependencies::

pip install -r requirements.txt

• • Loading Models::

from bagel_model import BagelModel

model = BagelModel.load_from_checkpoint("path/to/checkpoint")

• • Generating images::

text = "生成一个日落的图像"
image = model.generate_image(text)
image.save("output_image.png")

BAGEL's core strengths

• Powerful multimodal understanding: Based on a dual-encoder design, BAGEL supports the simultaneous capture of pixel-level and semantic-level features of an image to achieve a comprehensive understanding of multimodal data.
• High-quality generative capacity: Generate high-quality images based on text descriptions and support free-form image editing for complex creative needs.
• Advanced Technology Architecture: Based on expert mixing mechanism and tokenization processing, combined with massive data pre-training to improve the efficiency and performance of the model.
• Wide range of application scenarios: Applicable to content creation, 3D scene generation, visual learning, creative advertisement generation and user interaction experience, and other fields.
• Efficient training and optimization: Based on mixed-accuracy training and distributed training, it significantly improves training efficiency and reduces resource consumption.
• Open Source and Community Support: As an open source model, BAGEL provides code and model access with active community support for easy customization and optimization.

Who BAGEL is for

• content creator: Designers, artists, and advertisers who need to generate high-quality images, videos, or make creative designs.
• developers: Software developers and engineers who want to integrate multimodal functionality (e.g., image generation, video processing) into their projects.
• research worker: Researchers specializing in the fields of multimodal learning, artificial intelligence, and machine learning.
• educator: Teachers and educational institutions that need to present complex concepts to students through images or videos.
• business user: Businesses in e-commerce, advertising, entertainment, and other industries that need to improve user experience or content creation efficiency.