Add Portable zip Linux QuickStart (#12849)
* linux doc * update * Update ollama_portablze_zip_quickstart.md * Update ollama_portablze_zip_quickstart.md * Update ollama_portablze_zip_quickstart.zh-CN.md * Update ollama_portablze_zip_quickstart.md * meet code review * update * Add tips & troubleshooting sections for both Linux & Windows * Rebase * Fix based on comments * Small fix * Fix img * Update table for linux * Small fix --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
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Xin Qiu
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This guide demonstrates how to use [Ollama portable zip](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly) to directly run Ollama on Intel GPU with `ipex-llm` (without the need of manual installations).
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> [!NOTE]
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> Currently, IPEX-LLM only provides Ollama portable zip on Windows.
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## Table of Contents
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- [Prerequisites](#prerequisitesa)
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- [Windows Quickstart](#windows-quickstart)
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- [Prerequisites](#prerequisites)
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- [Step 1: Download and Unzip](#step-1-download-and-unzip)
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- [Step 2: Start Ollama Serve](#step-2-start-ollama-serve)
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- [Step 3: Run Ollama](#step-3-run-ollama)
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- [Tips & Troubleshooting](#tips--troubleshootings)
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- [Linux Quickstart](#linux-quickstart)
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- [Prerequisites](#prerequisites-1)
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- [Step 1: Download and Extract](#step-1-download-and-extract)
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- [Step 2: Start Ollama Serve](#step-2-start-ollama-serve-1)
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- [Step 3: Run Ollama](#step-3-run-ollama-1)
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- [Tips & Troubleshooting](#tips--troubleshooting)
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## Prerequisites
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## Windows Quickstart
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### Prerequisites
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Check your GPU driver version, and update it if needed:
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@ -23,13 +28,13 @@ Check your GPU driver version, and update it if needed:
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- For other Intel iGPU/dGPU, we recommend using GPU driver version [32.0.101.6078](https://www.intel.com/content/www/us/en/download/785597/834050/intel-arc-iris-xe-graphics-windows.html)
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## Step 1: Download and Unzip
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### Step 1: Download and Unzip
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Download IPEX-LLM Ollama portable zip from the [link](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly).
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Download IPEX-LLM Ollama portable zip for Windows users from the [link](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly).
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Then, extract the zip file to a folder.
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## Step 2: Start Ollama Serve
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### Step 2: Start Ollama Serve
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Double-click `start-ollama.bat` in the extracted folder to start the Ollama service. A window will then pop up as shown below:
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@ -37,7 +42,7 @@ Double-click `start-ollama.bat` in the extracted folder to start the Ollama serv
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_start_ollama.png" width=80%/>
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</div>
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## Step 3: Run Ollama
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### Step 3: Run Ollama
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You could then use Ollama to run LLMs on Intel GPUs as follows:
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@ -48,18 +53,71 @@ You could then use Ollama to run LLMs on Intel GPUs as follows:
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_run_ollama.png" width=80%/>
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</div>
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## Linux Quickstart
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### Prerequisites
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Check your GPU driver version, and update it if needed:
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- For client GPU, like A-series, B-series and integrated GPU, we recommend following [Intel client GPU driver installing guide](https://dgpu-docs.intel.com/driver/client/overview.html) to install your GPU driver.
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### Step 1: Download and Extract
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Download IPEX-LLM Ollama portable tgz for Ubuntu users from the [link](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly).
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Then open a terminal, extract the tgz file to a folder.
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```bash
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cd PATH/TO/DOWNLOADED/TGZ
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tar xvf [Downloaded tgz file]
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```
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### Step 2: Start Ollama Serve
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Enter the extracted folder, and run `start-ollama.sh` to start Ollama service.
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```bash
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cd PATH/TO/EXTRACTED/FOLDER
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./start-ollama.sh
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```
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<div align="center">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_start_ollama_ubuntu.png" width=80%/>
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</div>
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### Step 3: Run Ollama
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You could then use Ollama to run LLMs on Intel GPUs as follows:
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- Open another ternimal, and enter the extracted folder through `cd PATH/TO/EXTRACTED/FOLDER`
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- Run `./ollama run deepseek-r1:7b` (you may use any other model)
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<div align="center">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_run_ollama_ubuntu.png" width=80%/>
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</div>
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## Tips & Troubleshooting
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### Speed up model download using alternative sources
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Ollama by default downloads model from [Ollama library](https://ollama.com/library). By setting the environment variable `IPEX_LLM_MODEL_SOURCE` to `modelscope`/`ollama` before [run Ollama](#step-3-run-ollama), you could switch the source from which the model is downloaded first.
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Ollama by default downloads model from [Ollama library](https://ollama.com/library). By setting the environment variable `IPEX_LLM_MODEL_SOURCE` to `modelscope`/`ollama` **before Run Ollama**, you could switch the source from which the model is downloaded first.
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For example, if you would like to run `deepseek-r1:7b` but the download speed from Ollama library is quite slow, you could use [its model source](https://www.modelscope.cn/models/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF) from [ModelScope](https://www.modelscope.cn/models) instead, through:
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- Open "Command Prompt" (cmd), and navigate to the extracted folder by `cd /d PATH\TO\EXTRACTED\FOLDER`
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- For **Windows** users:
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- Open "Command Prompt", and navigate to the extracted folder by `cd /d PATH\TO\EXTRACTED\FOLDER`
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- Run `set IPEX_LLM_MODEL_SOURCE=modelscope` in "Command Prompt"
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- Run `ollama run deepseek-r1:7b`
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- For **Linux** users:
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- In a terminal other than the one for Ollama serve, navigate to the extracted folder by `cd PATH\TO\EXTRACTED\FOLDER`
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- Run `export IPEX_LLM_MODEL_SOURCE=modelscope` in the terminal
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- Run `./ollama run deepseek-r1:7b`
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> [!TIP]
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> Model downloaded with `set IPEX_LLM_MODEL_SOURCE=modelscope` will still show actual model id in `ollama list`, e.g.
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> ```
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By default, Ollama runs model with a context window of 2048 tokens. That is, the model can "remember" at most 2048 tokens of context.
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To increase the context length, you could set environment variable `IPEX_LLM_NUM_CTX` before [starting Ollama serve](#step-2-start-ollama-serve), as shwon below:
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To increase the context length, you could set environment variable `IPEX_LLM_NUM_CTX` **before Start Ollama Serve**, as shwon below (if Ollama serve is already running, please make sure to stop it first):
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- Open "Command Prompt" (cmd), and navigate to the extracted folder through `cd /d PATH\TO\EXTRACTED\FOLDER`
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- For **Windows** users:
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- Open "Command Prompt", and navigate to the extracted folder through `cd /d PATH\TO\EXTRACTED\FOLDER`
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- Set `IPEX_LLM_NUM_CTX` to the desired length in the "Command Prompt, e.g. `set IPEX_LLM_NUM_CTX=16384`
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- Start Ollama serve through `start-ollama.bat`
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- For **Linux** users:
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- In a terminal, navigate to the extracted folder through `cd PATH\TO\EXTRACTED\FOLDER`
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- Set `IPEX_LLM_NUM_CTX` to the desired length in the terminal, e.g. `export IPEX_LLM_NUM_CTX=16384`
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- Start Ollama serve through `./start-ollama.sh`
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> [!TIP]
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> `IPEX_LLM_NUM_CTX` has a higher priority than the `num_ctx` settings in a models' `Modelfile`.
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### Select specific GPU to run Ollama when multiple ones are available
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If your machine has multiple Intel GPUs, Ollama will by default runs on all of them.
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To specify which Intel GPU you would like Ollama to use, you could set environment variable `ONEAPI_DEVICE_SELECTOR` **before Start Ollama Serve**, as follows (if Ollama serve is already running, please make sure to stop it first):
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- Identify the id (e.g. 0, 1, etc.) for your multiple GPUs. You could find them in the logs of Ollama serve when loading any models, e.g.:
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<div align="center">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_multi_gpus.png" width=80%/>
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</div>
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- For **Windows** users:
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- Open "Command Prompt", and navigate to the extracted folder by `cd /d PATH\TO\EXTRACTED\FOLDER`
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- In the "Command Prompt", set `ONEAPI_DEVICE_SELECTOR` to define the Intel GPU you want to use, e.g. `set ONEAPI_DEVICE_SELECTOR=level_zero:0`, in which `0` should be changed to your desired GPU id
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- Start Ollama serve through `start-ollama.bat`
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- For **Linux** users:
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- In a terminal, navigate to the extracted folder by `cd PATH\TO\EXTRACTED\FOLDER`
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- Set `ONEAPI_DEVICE_SELECTOR` to define the Intel GPU you want to use, e.g. `export ONEAPI_DEVICE_SELECTOR=level_zero:0`, in which `0` should be changed to your desired GPU id
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- Start Ollama serve through `./start-ollama.sh`
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### Additional models supported after Ollama v0.5.4
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The currently Ollama Portable Zip is based on Ollama v0.5.4; in addition, the following new models have also been supported in the Ollama Portable Zip:
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| Model | Download | Model Link |
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| DeepSeek-R1 | `ollama run deepseek-r1` | [deepseek-r1](https://ollama.com/library/deepseek-r1) |
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| Openthinker | `ollama run openthinker` | [openthinker](https://ollama.com/library/openthinker) |
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| DeepScaleR | `ollama run deepscaler` | [deepscaler](https://ollama.com/library/deepscaler) |
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| Phi-4 | `ollama run phi4` | [phi4](https://ollama.com/library/phi4) |
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| Dolphin 3.0 | `ollama run dolphin3` | [dolphin3](https://ollama.com/library/dolphin3) |
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| Smallthinker | `ollama run smallthinker` | [smallthinker](https://ollama.com/library/smallthinker) |
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| Granite3.1-Dense | `ollama run granite3-dense` | [granite3.1-dense](https://ollama.com/library/granite3.1-dense) |
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| Granite3.1-Moe-3B | `ollama run granite3-moe` | [granite3.1-moe](https://ollama.com/library/granite3.1-moe) |
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| Model | Download (Windows) | Download (Linux) | Model Link |
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| - | - | - | - |
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| DeepSeek-R1 | `ollama run deepseek-r1` | `./ollama run deepseek-r1` | [deepseek-r1](https://ollama.com/library/deepseek-r1) |
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| Openthinker | `ollama run openthinker` | `./ollama run openthinker` | [openthinker](https://ollama.com/library/openthinker) |
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| DeepScaleR | `ollama run deepscaler` | `./ollama run deepscaler` | [deepscaler](https://ollama.com/library/deepscaler) |
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| Phi-4 | `ollama run phi4` | `./ollama run phi4` | [phi4](https://ollama.com/library/phi4) |
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| Dolphin 3.0 | `ollama run dolphin3` | `./ollama run dolphin3` | [dolphin3](https://ollama.com/library/dolphin3) |
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| Smallthinker | `ollama run smallthinker` |`./ollama run smallthinker` | [smallthinker](https://ollama.com/library/smallthinker) |
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| Granite3.1-Dense | `ollama run granite3-dense` | `./ollama run granite3-dense` | [granite3.1-dense](https://ollama.com/library/granite3.1-dense) |
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| Granite3.1-Moe-3B | `ollama run granite3-moe` | `./ollama run granite3-moe` | [granite3.1-moe](https://ollama.com/library/granite3.1-moe) |
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本指南演示如何使用 [Ollama portable zip](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly) 通过 `ipex-llm` 在 Intel GPU 上直接免安装运行 Ollama。
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> [!NOTE]
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> 目前，IPEX-LLM 仅在 Windows 上提供 Ollama portable zip。
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## 目录
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- [Windows用户指南](#windows用户指南)
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- [系统环境安装](#系统环境准备)
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- [步骤 1：下载和解压](#步骤-1下载和解压)
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- [步骤 2：启动 Ollama Serve](#步骤-2启动-ollama-serve)
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- [步骤 3：运行 Ollama](#步骤-3运行-ollama)
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- [提示和故障排除](#提示和故障排除)
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- [Linux用户指南](#linux用户指南)
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- [系统环境安装](#系统环境准备-1)
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- [步骤 1：下载和解压](#步骤-1下载和解压-1)
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- [步骤 2：启动 Ollama Serve](#步骤-2启动-ollama-serve-1)
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- [步骤 3：运行 Ollama](#步骤-3运行-ollama-1)
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## 系统环境准备
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## Windows用户指南
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### 系统环境准备
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检查你的 GPU 驱动程序版本，并根据需要进行更新：
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- 对于其他的 Intel 核显和独显，我们推荐使用 GPU 驱动版本 [32.0.101.6078](https://www.intel.com/content/www/us/en/download/785597/834050/intel-arc-iris-xe-graphics-windows.html)
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## 步骤 1：下载和解压
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### 步骤 1：下载和解压
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从此[链接](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly)下载 IPEX-LLM Ollama portable zip。
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然后，将 zip 文件解压到一个文件夹中。
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## 步骤 2：启动 Ollama Serve
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### 步骤 2：启动 Ollama Serve
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在解压后的文件夹中双击 `start-ollama.bat` 即可启动 Ollama Serve。随后会弹出一个窗口，如下所示：
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_start_ollama.png" width=80%/>
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</div>
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## 步骤 3：运行 Ollama
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### 步骤 3：运行 Ollama
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在 Intel GPUs 上使用 Ollama 运行 LLMs，如下所示：
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- 打开命令提示符（cmd），并通过在命令行输入指令 `cd /d PATH\TO\EXTRACTED\FOLDER` 进入解压后的文件夹
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- 在命令提示符中运行 `ollama run deepseek-r1:7（可以将当前模型替换为你需要的模型）
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- 在命令提示符中运行 `ollama run deepseek-r1:7b`（可以将当前模型替换为你需要的模型）
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<div align="center">
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_run_ollama.png" width=80%/>
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</div>
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## 提示和故障排除
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### 提示和故障排除
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### 通过切换源提升模型下载速度
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#### 通过切换源提升模型下载速度
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Ollama 默认从 [Ollama 库](https://ollama.com/library) 下载模型。在 [运行 Ollama](#步骤-3运行-ollama) 之前设置环境变量 `IPEX_LLM_MODEL_SOURCE` 为 `modelscope/ollama`，你可以切换模型的首选下载源。
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Ollama 默认从 [Ollama 库](https://ollama.com/library) 下载模型。通过在 [运行 Ollama](#步骤-3运行-ollama) 之前设置环境变量 `IPEX_LLM_MODEL_SOURCE` 为 `modelscope/ollama`，你可以切换模型的首选下载源。
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例如，如果你想运行 `deepseek-r1:7b` 但从 Ollama 库的下载速度较慢，可以通过如下方式改用 [ModelScope](https://www.modelscope.cn/models) 的 [模型源](https://www.modelscope.cn/models/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF)：
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例如，如果你想运行 `deepseek-r1:7b` 但从 Ollama 库的下载速度较慢，可以通过如下方式改用 [ModelScope](https://www.modelscope.cn/models) 上的 [模型源](https://www.modelscope.cn/models/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF)：
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- 打开 “命令提示符”（cmd），并通过 `cd /d PATH\TO\EXTRACTED\FOLDER` 命令进入解压后的文件夹
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- 在 “命令提示符” 中运行 `set IPEX_LLM_MODEL_SOURCE=modelscope`
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- 打开命令提示符（cmd），并通过 `cd /d PATH\TO\EXTRACTED\FOLDER` 命令进入解压后的文件夹
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- 在命令提示符中运行 `set IPEX_LLM_MODEL_SOURCE=modelscope`
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- 运行 `ollama run deepseek-r1:7b`
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> [!Tip]
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@ -68,20 +73,20 @@ Ollama 默认从 [Ollama 库](https://ollama.com/library) 下载模型。在 [
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> ```
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> 除了 `ollama run` 和 `ollama pull`，其他操作中模型应通过其实际 ID 进行识别，例如： `ollama rm modelscope.cn/unsloth/DeepSeek-R1-Distill-Qwen-7B-GGUF:Q4_K_M`
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### 在 Ollama 中增加上下文长度
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#### 在 Ollama 中增加上下文长度
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默认情况下，Ollama 使用 2048 个 token 的上下文窗口运行模型。也就是说，模型最多能 “记住” 2048 个 token 的上下文。
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要增加上下文长度，可以在 [启动 Ollama serve](#步骤-2启动-ollama-serve) 之前设置环境变量 `IPEX_LLM_NUM_CTX`，步骤如下：
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- 打开 “命令提示符”（cmd），并通过 `cd /d PATH\TO\EXTRACTED\FOLDER` 命令进入解压后的文件夹
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- 在 “命令提示符” 中将 `IPEX_LLM_NUM_CTX` 设置为所需长度，例如：`set IPEX_LLM_NUM_CTX=16384`
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- 打开命令提示符（cmd），并通过 `cd /d PATH\TO\EXTRACTED\FOLDER` 命令进入解压后的文件夹
|
||||
- 在命令提示符中将 `IPEX_LLM_NUM_CTX` 设置为所需长度，例如：`set IPEX_LLM_NUM_CTX=16384`
|
||||
- 通过运行 `start-ollama.bat` 启动 Ollama serve
|
||||
|
||||
> [!Tip]
|
||||
> `IPEX_LLM_NUM_CTX` 的优先级高于模型 `Modelfile` 中设置的 `num_ctx`。
|
||||
|
||||
### Ollama v0.5.4 之后支持的其他模型
|
||||
#### Ollama v0.5.4 之后支持的其他模型
|
||||
|
||||
当前的 Ollama Portable Zip 基于 Ollama v0.5.4；此外，以下新模型也已在 Ollama Portable Zip 中得到支持：
|
||||
|
||||
|
|
@ -95,3 +100,48 @@ Ollama 默认从 [Ollama 库](https://ollama.com/library) 下载模型。在 [
|
|||
| Smallthinker | `ollama run smallthinker` | [smallthinker](https://ollama.com/library/smallthinker) |
|
||||
| Granite3.1-Dense | `ollama run granite3-dense` | [granite3.1-dense](https://ollama.com/library/granite3.1-dense) |
|
||||
| Granite3.1-Moe-3B | `ollama run granite3-moe` | [granite3.1-moe](https://ollama.com/library/granite3.1-moe) |
|
||||
|
||||
|
||||
## Linux用户指南
|
||||
|
||||
### 系统环境准备
|
||||
|
||||
检查你的 GPU 驱动程序版本，并根据需要进行更新：
|
||||
|
||||
- 对于消费级显卡用户，如A系列，B系列和集成显卡，我们推荐按照[消费级显卡驱动安装指南](https://dgpu-docs.intel.com/driver/client/overview.html)来安装您的显卡驱动。
|
||||
|
||||
|
||||
### 步骤 1：下载和解压
|
||||
|
||||
从此[链接](https://github.com/intel/ipex-llm/releases/tag/v2.2.0-nightly)下载 IPEX-LLM Ollama portable tgz。
|
||||
|
||||
然后，开启一个终端，输入如下命令将 tgz 文件解压到一个文件夹中。
|
||||
```bash
|
||||
cd PATH/TO/DOWNLOADED/TGZ
|
||||
tar xvf [Downloaded tgz file]
|
||||
```
|
||||
|
||||
### 步骤 2：启动 Ollama Serve
|
||||
|
||||
进入解压后的文件夹，执行`./start-ollama.sh`启动 Ollama Serve：
|
||||
|
||||
[可选操作] 对于有多块显卡的用户，请编辑解压后文件夹中的 start-ollama.sh，并根据机器配置修改 ONEAPI_DEVICE_SELECTOR。默认情况下，Ollama 会使用所有显卡。
|
||||
```bash
|
||||
cd PATH/TO/EXTRACTED/FOLDER
|
||||
./start-ollama.sh
|
||||
```
|
||||
|
||||
<div align="center">
|
||||
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_start_ollama_ubuntu.png" width=80%/>
|
||||
</div>
|
||||
|
||||
### 步骤 3：运行 Ollama
|
||||
|
||||
在 Intel GPUs 上使用 Ollama 运行大语言模型，如下所示：
|
||||
|
||||
- 打开另外一个终端，并输入指令 `cd PATH/TO/EXTRACTED/FOLDER` 进入解压后的文件夹
|
||||
- 在终端中运行 `./ollama run deepseek-r1:7b`（可以将当前模型替换为你需要的模型）
|
||||
|
||||
<div align="center">
|
||||
<img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_portable_run_ollama_ubuntu.png" width=80%/>
|
||||
</div>
|
||||
|
|
|
|||
Loading…
Reference in a new issue