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fix docs

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  1. vllm_deployment_guide.md +3 -3
  2. vllm_deployment_guide_cn.md +3 -3
vllm_deployment_guide.md CHANGED
@@ -4,7 +4,7 @@
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  ## 📖 Introduction
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- We recommend using [vLLM](https://docs.vllm.ai/en/latest/) to deploy MiniMax-M1 model. Based on our testing, vLLM performs excellently when deploying this model, with the following features:
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  - 🔥 Outstanding service throughput performance
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  - ⚡ Efficient and intelligent memory management
@@ -17,7 +17,7 @@ The MiniMax-M1 model can run efficiently on a single server equipped with 8 H800
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  ### MiniMax-M1 Model Obtaining
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- You can download the model from our official HuggingFace repository: [MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1)
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  Download command:
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  ```
@@ -32,7 +32,7 @@ Or download using git:
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  ```bash
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  git lfs install
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- git clone https://huggingface.co/MiniMaxAI/MiniMax-M1
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  ```
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  ⚠️ **Important Note**: Please ensure that [Git LFS](https://git-lfs.github.com/) is installed on your system, which is necessary for completely downloading the model weight files.
 
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  ## 📖 Introduction
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+ We recommend using [vLLM](https://docs.vllm.ai/en/latest/) to deploy [MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k) model. Based on our testing, vLLM performs excellently when deploying this model, with the following features:
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  - 🔥 Outstanding service throughput performance
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  - ⚡ Efficient and intelligent memory management
 
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  ### MiniMax-M1 Model Obtaining
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+ You can download the model from our official HuggingFace repository: [MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k)
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  Download command:
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  ```
 
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  ```bash
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  git lfs install
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+ git clone https://huggingface.co/MiniMaxAI/MiniMax-M1-40k
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  ```
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  ⚠️ **Important Note**: Please ensure that [Git LFS](https://git-lfs.github.com/) is installed on your system, which is necessary for completely downloading the model weight files.
vllm_deployment_guide_cn.md CHANGED
@@ -2,7 +2,7 @@
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  ## 📖 简介
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- 我们推荐使用 [vLLM](https://docs.vllm.ai/en/latest/) 来部署 [MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1) 模型。经过我们的测试，vLLM 在部署这个模型时表现出色，具有以下特点：
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  - 🔥 卓越的服务吞吐量性能
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  - ⚡ 高效智能的内存管理机制
@@ -15,7 +15,7 @@ MiniMax-M1 模型可在单台配备8个H800或8个H20 GPU的服务器上高效
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  ### MiniMax-M1 模型获取
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- 您可以从我们的官方 HuggingFace 仓库下载模型：[MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1)
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  下载命令：
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  ```
@@ -30,7 +30,7 @@ export HF_ENDPOINT=https://hf-mirror.com
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  ```bash
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  git lfs install
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- git clone https://huggingface.co/MiniMaxAI/MiniMax-M1
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  ```
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  ⚠️ **重要提示**：请确保系统已安装 [Git LFS](https://git-lfs.github.com/)，这对于完整下载模型权重文件是必需的。
 
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  ## 📖 简介
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+ 我们推荐使用 [vLLM](https://docs.vllm.ai/en/latest/) 来部署 [MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k) 模型。经过我们的测试，vLLM 在部署这个模型时表现出色，具有以下特点：
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  - 🔥 卓越的服务吞吐量性能
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  - ⚡ 高效智能的内存管理机制
 
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  ### MiniMax-M1 模型获取
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+ 您可以从我们的官方 HuggingFace 仓库下载模型：[MiniMax-M1](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k)
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  下载命令：
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  ```
 
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  ```bash
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  git lfs install
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+ git clone https://huggingface.co/MiniMaxAI/MiniMax-M1-40k
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  ```
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  ⚠️ **重要提示**：请确保系统已安装 [Git LFS](https://git-lfs.github.com/)，这对于完整下载模型权重文件是必需的。