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README.md
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- text_generation
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---
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DA2.0-flash-preview
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**LLaDA2-flash-preview** is a diffusion language model featuring a 100BA6B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA2.0 series, it is optimized for practical applications.
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<div align="center">
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<img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*kLORSaRfSK8AAAAAgIAAAAgAemJ7AQ/original" width="800" />
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## 📦 Model Variants
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| Model ID | Description | Hugging Face Link |
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| --- | --- | --- |
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| `inclusionAI/LLaDA2-mini-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-mini-preview) |
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| `inclusionAI/LLaDA2-flash-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-flash-preview) |
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---
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from transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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model_path = "/path/to/LLaDA2-mini-preview"
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device = "auto"
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model = AutoModelForCausalLM.from_pretrained(
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model_path, trust_remote_code=True, device_map=device
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- text_generation
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---
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DA2.0-flash-preview
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+
**LLaDA2.0-flash-preview** is a diffusion language model featuring a 100BA6B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA2.0 series, it is optimized for practical applications.
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<div align="center">
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<img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*kLORSaRfSK8AAAAAgIAAAAgAemJ7AQ/original" width="800" />
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## 📦 Model Variants
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| Model ID | Description | Hugging Face Link |
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| 67 |
| --- | --- | --- |
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| 68 |
+
| `inclusionAI/LLaDA2.0-mini-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-mini-preview) |
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| 69 |
+
| `inclusionAI/LLaDA2.0-flash-preview` | Instruction-tuned model, ready for downstream applications. | [🤗 Model Card](https://huggingface.co/inclusionAI/LLaDA2.0-flash-preview) |
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---
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from transformers import AutoModelForCausalLM
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from transformers import AutoTokenizer
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model_path = "/path/to/LLaDA2.0-mini-preview"
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device = "auto"
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model = AutoModelForCausalLM.from_pretrained(
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model_path, trust_remote_code=True, device_map=device
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