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README.md

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+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.
+
+