Qwen1.5 MoE A2.7B Chat Introduction Qwen1.5 MoE is a transformer based MoE decoder only language model pretrained on a large amount of data. For more details, please refer to our blog post and GitHub repo. Model Details 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 achieching 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 . Training details We pretrained the models with a large amount of data, and we post trained the models with both supervised finetuning and direct preference optimization. Requirements 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: Quickstart Here provides a code snippet with apply chat template to show you how to load the tokenizer and model and how to gene…
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