qwen:1.8b-text-v1.5-q3_K_L

6 種模型尺寸，包含 0.5B、1.8B、4B (預設)、7B、14B、32B (全新) 與 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效能提升
基礎模型與聊天模型皆支援多語言
所有尺寸的模型皆穩定支援 32K 上下文長度

原始 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。
大規模高品質訓練語料庫：模型在超過 2.2 兆個 tokens 的資料上進行預訓練，包含中文、英文、多語言文本、程式碼與數學，涵蓋一般與專業領域。預訓練語料庫的分布已透過大量的消融實驗進行最佳化。
良好效能：Qwen 支援長上下文長度 (1.8b、7b 與 14b 參數模型為 8K，72b 參數模型為 32K)，並且在多項中文與英文下游評估任務 (包含常識、推理、程式碼、數學等) 上顯著超越現有的同規模開源模型，甚至在多項基準測試中超越一些更大規模的模型。
更全面的詞彙覆蓋率：相較於其他基於中文與英文詞彙的開源模型，Qwen 使用超過 150K tokens 的詞彙。此詞彙對多種語言更友善，讓使用者能夠直接增強特定語言的能力，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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