14B total / 7B active Mixture-of-Transformer-Experts model unifying multimodal understanding and generation. Dual-encoder (SigLIP-L + FLUX.1 VAE) with specialized language and vision decoder experts.
A strong 14B-parameter MoE image generator from Bytedance. Treat the modality benchmarks above as the leading indicator of fit — composite scoring across modalities is still maturing.
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BAGEL-7B-MoT is a unified multimodal foundation model from ByteDance designed to bridge the gap between visual understanding and image generation. Built on a Mixture-of-Transformer-Experts (MoT) architecture, it utilizes 14 billion total parameters, with only 7 billion active during any given forward pass. This design provides the reasoning depth of a larger model while maintaining the inference efficiency of a 7B-class model. Unlike typical Vision-Language Models (VLMs) that specialize in either description or generation, BAGEL-7B-MoT is an "any-to-any" model capable of processing interleaved text and image data to perform complex reasoning, high-fidelity image synthesis, and sophisticated image editing within a single weights file.
The model is positioned as a direct competitor to high-end open-source VLMs like Qwen2.5-VL and InternVL-2.5. By leveraging a dual-encoder system—combining a SigLIP-L for semantic extraction and a FLUX.1 VAE for pixel-level reconstruction—ByteDance has created a tool that rivals specialist image generators like Stable Diffusion 3 (SD3) while maintaining state-of-the-art performance on benchmarks like MathVista and MMBench. For developers looking to run BAGEL-7B-MoT locally, it represents one of the most versatile multimodal assets available under the permissive Apache 2.0 license.
The defining technical feature of BAGEL-7B-MoT is its Mixture-of-Transformer-Experts (MoT) framework. In standard Mixture-of-Experts (MoE) architectures, the model routes tokens to specialized feed-forward networks. BAGEL-7B-MoT extends this by specializing the transformer blocks themselves to handle the distinct modalities of vision and language. This allows the model to scale its knowledge base to 14B parameters without the linear increase in compute cost typically associated with dense models of that size.
To achieve "world-modeling" capabilities, BAGEL-7B-MoT uses a sophisticated input pipeline:
Because only 7B parameters are active during inference, the model offers a high tokens-per-second rate compared to dense 14B models. However, users must account for the full 14B parameter count when calculating VRAM requirements, as the entire model must reside in memory to avoid significant latency penalties from offloading experts to system RAM.
BAGEL-7B-MoT is built for practitioners who need a "Swiss Army Knife" for visual tasks. Its training on large-scale interleaved data enables it to follow complex instructions that involve both seeing and creating.
Unlike many VLMs that produce low-resolution or artifact-heavy "thumbnails," BAGEL-7B-MoT produces text-to-image results competitive with SD3. It excels in:
The model outperforms many larger dense models on visual benchmarks. Concrete use cases include:
To run BAGEL-7B-MoT locally, hardware selection is dictated by the 14B total parameter count. While only 7B parameters are active per token, the full 14B must be loaded into VRAM to maintain acceptable performance.
For most local deployments, GGUF or EXL2 formats are recommended.
The quickest way to get started is via Ollama, which simplifies the management of MoE architectures. For those requiring more granular control over the image generation pipeline, the official bagel-mot library or a custom implementation using Hugging Face transformers and accelerate is recommended.
When evaluating BAGEL-7B-MoT, it is most frequently compared to Qwen2.5-VL-7B and Llama-3.2-Vision-11B.
The primary tradeoff with BAGEL-7B-MoT is the VRAM footprint. You are paying the "memory tax" of a 14B model to get the "compute speed" of a 7B model. For users with 24GB GPUs, this is an excellent trade-off; for those on 8GB cards, a smaller dense model like Moondream2 or a heavily quantized Qwen2.5-VL-3B may be more practical.
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