35 lines
1.4 KiB
Markdown
35 lines
1.4 KiB
Markdown
---
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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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