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README.md
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In addition, Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by Jingze Shi, it only allows text input and text generation, for detailed algorithm and model architecture, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), the ongoing research repository is [Wonderful Matrices](https://github.com/LoserCheems/WonderfulMatrices).
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## Uses
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```python
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> TODO: The larger model is under training and will be uploaded soon.
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| [Doge-20M](https://huggingface.co/
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| [Doge-60M](https://huggingface.co/
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- Image: nvcr.io/nvidia/pytorch:24.10-py3
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- Hardware: 1x NVIDIA RTX 4090
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- Software: Transformers
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## Citation
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```bibtex
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In addition, Doge uses Dynamic Mask Attention as sequence transformation and can use Multi-Layer Perceptron or Cross Domain Mixture of Experts as state transformation. Dynamic Mask Attention allows the Transformer to use self-attention during training and state space during inference, and Cross Domain Mixture of Experts can directly inherit the weights of Multi-Layer Perceptron for further training. This model is trained by Jingze Shi, it only allows text input and text generation, for detailed algorithm and model architecture, please refer to [Wonderful Matrices](https://arxiv.org/abs/2412.11834), the ongoing research repository is [Wonderful Matrices](https://github.com/LoserCheems/WonderfulMatrices).
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## Uses
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```python
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> TODO: The larger model is under training and will be uploaded soon.
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**Training**:
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| Model | Training Data | Epochs | Steps | Content Length | Tokens | LR | Batch Size | Precision |
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| [Doge-20M](https://huggingface.co/JingzeShi/Doge-20M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 2 | 10k | 2048 | 5B | 8e-4 | 0.25M | bfloat16 |
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| [Doge-60M](https://huggingface.co/JingzeShi/Doge-60M) | [HuggingFaceTB/smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) | 2 | 20k | 2048 | 20B | 6e-4 | 0.5M | bfloat16 |
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**Evaluation**:
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| Model | TriviaQA | MMLU | ARC | PIQA | HellaSwag | OBQA | Winogrande |
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| [Doge-20M](https://huggingface.co/JingzeShi/Doge-20M) | - | 26.01 | 36.15 | 56.26 | 26.60 | 26.60 | 50.12 |
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| [Doge-60M](https://huggingface.co/JingzeShi/Doge-60M) | - | 25.81 | 45.49 | 61.37 | 29.65 | 27.40 | 52.57 |
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**Environment**:
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- Image: nvcr.io/nvidia/pytorch:24.10-py3
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- Hardware: 1x NVIDIA RTX 4090
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- Software: Transformers
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## Citation
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```bibtex
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