| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| Qwen1.5-MoE-A2.7B - bnb 8bits | |
| - Model creator: https://huggingface.co/Qwen/ | |
| - Original model: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/ | |
| 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](https://qwenlm.github.io/blog/qwen-moe/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5). | |
| ## 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: | |
| ``` | |
| KeyError: 'qwen2_moe'. | |
| ``` | |
| ## 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. | |