Source:

https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B-Chat

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.