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--- |
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license: other |
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license_name: tongyi-qianwen |
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license_link: >- |
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https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- pretrained |
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- moe |
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--- |
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# Qwen1.5-MoE-A2.7B |
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## Introduction |
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Qwen1.5-MoE is a transformer-based MoE decoder-only language model pretrained on a large amount of data. |
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For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen-moe/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). |
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## Model Details |
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Qwen1.5-MoE employs Mixture of Experts (MoE) architecture, where the models are upcycled from dense language models. For instance, `Qwen1.5-MoE-A2.7B` is upcycled from `Qwen-1.8B`. It has 14.3B parameters in total and 2.7B activated parameters during runtime, while achieving comparable performance to `Qwen1.5-7B`, it only requires 25% of the training resources. We also observed that the inference speed is 1.74 times that of `Qwen1.5-7B`. |
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## Requirements |
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The code of Qwen1.5-MoE has been in the latest Hugging face transformers and we advise you to build from source with command `pip install git+https://github.com/huggingface/transformers`, or you might encounter the following error: |
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``` |
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KeyError: 'qwen2_moe'. |
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``` |
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## Usage |
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We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. |
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