qwen:32b-text-v1.5-q4_0 - Ollama 框架

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有显着性能提升
基础模型和聊天模型均支持多语言
所有尺寸的模型均稳定支持 32K 上下文长度

最初的 Qwen 模型提供四种不同的参数大小：1.8B、7B、14B 和 72B。

特性

低成本部署: 推理所需的最小内存小于 2GB。
大规模高质量训练语料库: 模型在超过 2.2 万亿个 token 上进行预训练，包括中文、英文、多语种文本、代码和数学，涵盖一般和专业领域。通过大量消融实验优化了预训练语料库的分布。
良好的性能: Qwen 支持长上下文长度 (1.8b、7b 和 14b 参数模型上为 8K，72b 参数模型上为 32K)，并且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) 上显着超过了现有类似规模的开源模型，甚至在某些基准测试中超过了一些更大规模的模型。
更全面的词汇覆盖: 与其他基于中文和英文词汇表的开源模型相比，Qwen 使用超过 15 万个 token 的词汇表。该词汇表对多种语言更加友好，使用户可以直接进一步增强某些语言的能力，而无需扩展词汇表。
系统提示: Qwen 可以通过使用系统提示来实现角色扮演、语言风格转移、任务设置和行为设置。

参考

GitHub

Hugging Face

Qwen 2 is now available [here](https://ollama.ac.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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