Quantization made by Richard Erkhov.
Qwen1.5-MoE-A2.7B - bnb 4bits
Original model description:
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
- moe
Qwen1.5-MoE-A2.7B
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 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.
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:
Usage
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.
