qwen:32b-text-v1.5-q4_K_S

6 種模型尺寸，包括 0.5B、1.8B、4B (預設)、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
聊天模型在人類偏好方面有顯著的性能提升
基礎模型和聊天模型均支援多語言
所有尺寸的模型均穩定支援 32K 上下文長度

原始的 Qwen 模型提供四種不同的參數尺寸：1.8B、7B、14B 和 72B。

功能特色

低成本部署：推論所需的最低記憶體小於 2GB。
大規模高品質的訓練語料庫：模型在超過 2.2 兆個 token 上進行預訓練，包括中文、英文、多語言文本、程式碼和數學，涵蓋一般和專業領域。透過大量的消融實驗，優化了預訓練語料庫的分佈。
良好的性能：Qwen 支援長上下文長度（1.8b、7b 和 14b 參數模型為 8K，72b 參數模型為 32K），並且在多個中文和英文下游評估任務（包括常識、推理、程式碼、數學等）中顯著超越了現有的同等規模的開源模型，甚至在幾個基準測試中超越了一些更大規模的模型。
更全面的詞彙覆蓋率：與其他基於中文和英文詞彙的開源模型相比，Qwen 使用了超過 15 萬個 token 的詞彙。這個詞彙對多種語言更友善，讓使用者可以直接進一步增強某些語言的能力，而無需擴展詞彙。
系統提示：Qwen 可以透過使用系統提示來實現角色扮演、語言風格轉換、任務設定和行為設定。

參考資料

GitHub

Hugging Face

Qwen 2 is now available [here](https://ollama.dev.org.tw/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://huggingface.co/Qwen)

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