Instructions to use inclusionAI/LLaDA2.0-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LLaDA2.0-flash with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/LLaDA2.0-flash", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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## 🚀 Performance Highlights
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+ **Leading MoE Architecture**:
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The open-source **Mixture-of-Experts (MoE) diffusion large language model**
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+ **Efficient Inference**:
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With **100 billion total parameters**, only **6.1 billion** are activated during inference. LLaDA2.0-flash significantly reduces computational costs while outperforming open-source dense models of similar scale.
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+ **Impressive Performance on Code & Complex Reasoning**:
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## 🚀 Performance Highlights
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+ **Leading MoE Architecture**:
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The open-source **Mixture-of-Experts (MoE) diffusion large language model** continually trained on the Ling2.0 series with approximately **20 trillion tokens**.
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+ **Efficient Inference**:
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With **100 billion total parameters**, only **6.1 billion** are activated during inference. LLaDA2.0-flash significantly reduces computational costs while outperforming open-source dense models of similar scale.
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+ **Impressive Performance on Code & Complex Reasoning**:
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