qwen

6 种模型尺寸，包括 0.5B、1.8B、4B（默认）、7B、14B、32B（新增）和 72B
- ollama run qwen:0.5b
- ollama run qwen:1.8b
- ollama run qwen:4b
- ollama run qwen:7b
- ollama run qwen:14b
- ollama run qwen:32b
- ollama run qwen:72b
- ollama run qwen:110b
聊天模型在人类偏好方面取得显著性能提升
基础模型和聊天模型都支持多语言
所有尺寸的模型都稳定支持 32K 上下文长度

原始 Qwen 模型提供四种不同的参数尺寸：1.8B、7B、14B 和 72B。

功能

低成本部署：推理的最低内存需求小于 2GB。
大规模高质量训练语料库：模型在超过 2.2 万亿个词元上进行预训练，包括中文、英文、多语言文本、代码和数学，涵盖一般领域和专业领域。预训练语料库的分布经过大量消融实验进行了优化。
良好的性能： Qwen 支持较长的上下文长度（在 1.8b、7b 和 14b 参数模型上为 8K，在 72b 参数模型上为 32K），在多个中文和英文下游评估任务（包括常识、推理、代码、数学等）上显著优于同等规模的现有开源模型，甚至在一些基准测试中超过了一些更大的模型。
更全面的词汇覆盖范围：与其他基于中文和英文词汇的开源模型相比，Qwen 使用了超过 150K 个词元的词汇。此词汇更友好地支持多种语言，使用户能够直接进一步增强对某些语言的能力，而无需扩展词汇。
系统提示： Qwen 可以通过使用系统提示来实现角色扮演、语言风格迁移、任务设置和行为设置。

参考

GitHub

Hugging Face

Qwen 2 is now available [here](https://ollama.org.cn/library/qwen2).

Qwen is a series of transformer-based large language models by Alibaba Cloud, pre-trained on a large volume of data, including web texts, books, code, etc.

### New in Qwen 1.5

- 6 model sizes, including 0.5B, 1.8B, 4B (default), 7B, 14B, 32B (new) and 72B
  * `ollama run qwen:0.5b`
  * `ollama run qwen:1.8b`
  * `ollama run qwen:4b` 
  * `ollama run qwen:7b` 
  * `ollama run qwen:14b`
  * `ollama run qwen:32b`
  * `ollama run qwen:72b`
  * `ollama run qwen:110b`
- Significant performance improvement in human preference for chat models
- Multilingual support of both base and chat models
- Stable support of 32K context length for models of all sizes

The original Qwen model is offered in four different parameter sizes: 1.8B, 7B, 14B, and 72B.

## Features

* **Low-cost deployment**: the minimum memory requirement for inference is less than 2GB.

* **Large-scale high-quality training corpora**: Models are pre-trained on over 2.2 trillion tokens, including Chinese, English, multilingual texts, code, and mathematics, covering general and professional fields. The distribution of the pre-training corpus has been optimized through a large number of ablation experiments.

* **Good performance**: Qwen supports long context lengths (8K on the `1.8b`, `7b` and `14b` parameter models, and 32K on the `72b` parameter model), and significantly surpasses existing open-source models of similar scale on multiple Chinese and English downstream evaluation tasks (including common-sense, reasoning, code, mathematics, etc.), and even surpasses some larger-scale models in several benchmarks.

* **More comprehensive vocabulary coverage**: Compared with other open-source models based on Chinese and English vocabularies, Qwen uses a vocabulary of over 150K tokens. This vocabulary is more friendly to multiple languages, enabling users to directly further enhance the capability for certain languages without expanding the vocabulary.

* **System prompt**: Qwen can realize role playing, language style transfer, task setting, and behavior-setting by using a system prompt.

## Reference

[GitHub](https://github.com/QwenLM/Qwen)

[Hugging Face](https://hugging-face.cn/Qwen)

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