📖 docs: Local Qwen for Ollama (lobehub#1723)

* 📖 docs: Local Qwen for Ollama (lobehub#1622)

* 📝 docs: update docs

* 📝 docs: update docs

* 📝 docs: update docs

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Co-authored-by: Maple Gao <esanisa@gmail.com>

docs/usage/providers/ollama/qwen.mdx

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+---
+title: Using the Local Qwen Model
+image: https://github.com/lobehub/lobe-chat/assets/28616219/689e19ea-3003-4e15-a1d5-b7343d5ba898
+---
+
+# Using the Local Qwen Model
+
+<Image alt={'Using Qwen in LobeChat'} cover src={'https://github.com/lobehub/lobe-chat/assets/28616219/7a5fd01a-9fed-49c1-93a3-422269213f19'} />
+
+[Qwen](https://github.com/QwenLM/Qwen1.5) is a large language model (LLM) open-sourced by Alibaba Cloud. It is officially defined as a constantly evolving AI large model, and it achieves more accurate Chinese recognition capabilities through more training set content.
+
+<Video src="https://github.com/lobehub/lobe-chat/assets/28616219/31e5f625-8dc4-4a5f-a5fd-d28d0457782d" />
+
+Now, through the integration of LobeChat and [Ollama](https://ollama.com/), you can easily use Qwen in LobeChat. This document will guide you on how to use the local deployment version of Qwen in LobeChat:
+
+<Steps>
+  ## Local Installation of Ollama
+
+  First, you need to install Ollama. For the installation process, please refer to the [Ollama Usage Document](/en/usage/providers/ollama).
+
+  ## Pull the Qwen Model to Local with Ollama
+
+  After installing Ollama, you can install the Qwen model with the following command, taking the 14b model as an example:
+
+  ```bash
+  ollama pull qwen:14b
+  ```
+  <Callout type={'info'}>
+    The local version of Qwen provides different model sizes to choose from. Please refer to the
+    [Qwen's Ollama integration page](https://ollama.com/library/qwen) to understand how to choose the
+    model size.
+  </Callout>
+
+  <Image alt={'Use Ollama Pull Qwen Model'} height={473} inStep src={'https://github.com/lobehub/lobe-chat/assets/1845053/fe34fdfe-c2e4-4d6a-84d7-4ebc61b2516a'} />
+
+  ### Select the Qwen Model
+
+  In the LobeChat conversation page, open the model selection panel, and then select the Qwen model.
+
+  <Image alt={'Choose Qwen Model'} height={430} inStep src={'https://github.com/lobehub/lobe-chat/assets/28616219/e0608cca-f62f-414a-bc55-28a61ba21f14'} />
+
+  <Callout type={'info'}>
+    If you do not see the Ollama provider in the model selection panel, please refer to [Integration with Ollama](/en/self-hosting/examples/ollama) to learn how to enable the Ollama provider in LobeChat.
+  </Callout>
+</Steps>
+
+Next, you can have a conversation with the local Qwen model in LobeChat.

docs/usage/providers/ollama/qwen.zh-CN.mdx

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+---
+title: 使用本地通义千问 Qwen 模型
+image: https://github.com/lobehub/lobe-chat/assets/28616219/689e19ea-3003-4e15-a1d5-b7343d5ba898
+---
+
+# 使用本地通义千问 Qwen 模型
+
+<Image alt={'在 LobeChat 中使用 Qwen'} cover src={'https://github.com/lobehub/lobe-chat/assets/28616219/7a5fd01a-9fed-49c1-93a3-422269213f19'} />
+
+[通义千问](https://github.com/QwenLM/Qwen1.5) 是阿里云开源的一款大语言模型（LLM），官方定义是一个不断进化的 AI 大模型，并通过更多的训练集内容达到更精准的中文识别能力。
+
+<Video src="https://github.com/lobehub/lobe-chat/assets/28616219/31e5f625-8dc4-4a5f-a5fd-d28d0457782d" />
+
+现在，通过 LobeChat 与 [Ollama](https://ollama.com/) 的集成，你可以轻松地在 LobeChat 中使用 通义千问。
+
+本文档将指导你如何在 LobeChat 中使用通义千问本地部署版：
+
+<Steps>
+  ### 本地安装 Ollama
+
+  首先，你需要安装 Ollama，安装过程请查阅 [Ollama 使用文件](/zh/usage/providers/ollama)。
+
+  ### 用 Ollama 拉取 Qwen 模型到本地
+
+  在安装完成 Ollama 后，你可以通过以下命令安装 Qwen 模型，以 14b 模型为例：
+
+  ```bash
+  ollama pull qwen:14b
+  ```
+
+  <Image alt={'使用 Ollama 拉取 Qwen 模型'} height={473} inStep src={'https://github.com/lobehub/lobe-chat/assets/1845053/fe34fdfe-c2e4-4d6a-84d7-4ebc61b2516a'} />
+
+  ### 选择 Qwen 模型
+
+  在会话页面中，选择模型面板打开，然后选择 Qwen 模型。
+
+  <Image alt={'模型选择面板中选择 Qwen 模型'} height={430} inStep src={'https://github.com/lobehub/lobe-chat/assets/28616219/e0608cca-f62f-414a-bc55-28a61ba21f14'} />
+
+  <Callout type={'info'}>
+    如果你没有在模型选择面板中看到 Ollama 服务商，请查阅 [与 Ollama
+    集成](/zh/self-hosting/examples/ollama) 了解如何在 LobeChat 中开启 Ollama 服务商。
+  </Callout>
+</Steps>
+
+接下来，你就可以使用 LobeChat 与本地 Qwen 模型对话了。