**Teutonic-II 110B Genesis** is an open-weight, compute-efficient sparse Mixture-of-Experts (MoE) language model with approximately **110B total parameters** and an estimated **~7.3B activated parameters per token**. The **Genesis checkpoint is initialized with random weights and has not yet undergone pretraining**, providing a clean starting point for continued pretraining and decentralized model improvement through **Teutonic** on **Bittensor Subnet 3**. The model is built on a modified **MiMoV2-style architecture**, combining sparse MoE layers with hybrid Global and Sliding Window Attention, Grouped Query Attention, asymmetric QK/V head dimensions, partial Rotary Position Embeddings, and attention-sink mechanisms. **In addition to adopting MiMoV2 architectural concepts, the Teutonic team introduces a modification to the MoE implementation that enables expert sharing.** Teutonic-II 110B Genesis is designed as a compute-efficient large-scale sparse language model architecture for continued pretraining and decentralized model improvement.
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GGUF-converted weights of openbmb/VoxCPM2 for the C++/ggml inference engine llama.cpp-omni (`tools/omni/voxcpm2`). These let you run VoxCPM2 text-to-speech and zero-shot voice cloning **natively on CPU / Metal / CUDA / Vulkan** via ggml — no PyTorch runtime required. VoxCPM2 outputs 48 kHz mono audio. You need one BaseLM (F16 or Q8_0) + the Acoustic file. Q8_0 halves the BaseLM download size with negligible quality loss and a slight speed boost.
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We present Depth Anything 3 (DA3), a model that predicts spatially consistent geometry from an arbitrary number of visual inputs, with or without known camera poses. In pursuit of minimal modeling, DA3 yields two key insights: a single plain transformer (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization, and a singular depth-ray prediction target obviates the need for complex multi-task learning. Through our teacher-student training paradigm, the model achieves a level of detail and generalization on par with Depth Anything 2 (DA2). We establish a new visual geometry benchmark covering camera pose estimation, any-view geometry and visual rendering. On this benchmark, DA3 sets a new state-of-the-art across all tasks, surpassing prior SOTA VGGT by an average of 44.3% in camera pose accuracy and 25.1% in geometric accuracy. Moreover, it outperforms DA2 in monocular depth estimation. All models are trained exclusively on public academic datasets. DA3 Base model for multi-view depth estimation and camera pose estimation. Compact foundation model with unified depth-ray representation. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only
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DA3 Giant model for multi-view depth estimation, camera pose estimation, and 3D Gaussian estimation. This is the flagship foundation model with unified depth-ray representation. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Giant model for multi-view depth estimation, camera pose estimation, and 3D Gaussian estimation. This is the flagship foundation model with unified depth-ray representation. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Large model for multi-view depth estimation and camera pose estimation. Foundation model with unified depth-ray representation. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Large model for multi-view depth estimation and camera pose estimation. Foundation model with unified depth-ray representation. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Small model for multi-view depth estimation and camera pose estimation. Efficient foundation model with unified depth-ray representation. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Metric Large model specialized for metric depth estimation in monocular settings, ideal for applications requiring real-world scale. Canonical metric depth; multiplying by focal length gives metric depth. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Monocular Large model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g., Depth Anything 2), it directly predicts depth, resulting in superior geometric accuracy. - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501
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DA3 Nested model combining the any-view Giant model with the metric Large model for metric-scale visual geometry reconstruction. This is our recommended model that combines all capabilities. - ✅ Relative Depth - ✅ Pose Estimation - ✅ Pose Conditioning - ✅ 3D Gaussians - ✅ Metric Depth - ✅ Sky Segmentation - **Developed by:** ByteDance Seed Team - **Model Type:** Vision Transformer for Visual Geometry - **Architecture:** Plain transformer with unified depth-ray representation - **Training Data:** Public academic datasets only
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