ipex-llm/docs/readthedocs/source/doc/LLM/Quickstart/ollama_quickstart.md

Run Ollama with IPEX-LLM on Intel GPU

ollama/ollama is popular framework designed to build and run language models on a local machine; you can now use the C++ interface of ipex-llm as an accelerated backend for ollama running on Intel GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max).

See the demo of running LLaMA2-7B on Intel Arc GPU below.

.. note::

  Our current version is consistent with `v0.1.34 <https://github.com/ollama/ollama/releases/tag/v0.1.34>`_ of ollama.

Quickstart

1 Install IPEX-LLM for Ollama

IPEX-LLM's support for ollama now is available for Linux system and Windows system.

Visit Run llama.cpp with IPEX-LLM on Intel GPU Guide, and follow the instructions in section Prerequisites to setup and section Install IPEX-LLM cpp to install the IPEX-LLM with Ollama binaries.

After the installation, you should have created a conda environment, named llm-cpp for instance, for running ollama commands with IPEX-LLM.

2. Initialize Ollama

Activate the llm-cpp conda environment and initialize Ollama by executing the commands below. A symbolic link to ollama will appear in your current directory.

.. tabs::
   .. tab:: Linux

      .. code-block:: bash
      
         conda activate llm-cpp
         init-ollama

   .. tab:: Windows

      Please run the following command with **administrator privilege in Anaconda Prompt**.

      .. code-block:: bash
      
         conda activate llm-cpp
         init-ollama.bat

Now you can use this executable file by standard ollama's usage.

3 Run Ollama Serve

You may launch the Ollama service as below:

.. tabs::
   .. tab:: Linux

      .. code-block:: bash

         export OLLAMA_NUM_GPU=999
         export no_proxy=localhost,127.0.0.1
         export ZES_ENABLE_SYSMAN=1
         source /opt/intel/oneapi/setvars.sh
         export SYCL_CACHE_PERSISTENT=1

         ./ollama serve

   .. tab:: Windows

      Please run the following command in Anaconda Prompt.

      .. code-block:: bash

         set OLLAMA_NUM_GPU=999
         set no_proxy=localhost,127.0.0.1
         set ZES_ENABLE_SYSMAN=1
         set SYCL_CACHE_PERSISTENT=1

         ollama serve

.. note::

  Please set environment variable ``OLLAMA_NUM_GPU`` to ``999`` to make sure all layers of your model are running on Intel GPU, otherwise, some layers may run on CPU.

.. tip::

  If your local LLM is running on Intel Arc™ A-Series Graphics with Linux OS (Kernel 6.2), it is recommended to additionaly set the following environment variable for optimal performance before executing `ollama serve`:

  .. code-block:: bash

      export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1

.. note::

  To allow the service to accept connections from all IP addresses, use `OLLAMA_HOST=0.0.0.0 ./ollama serve` instead of just `./ollama serve`.

The console will display messages similar to the following:

4 Pull Model

Keep the Ollama service on and open another terminal and run ./ollama pull <model_name> in Linux (ollama.exe pull <model_name> in Windows) to automatically pull a model. e.g. dolphin-phi:latest:

5 Using Ollama

Using Curl

Using curl is the easiest way to verify the API service and model. Execute the following commands in a terminal. Replace the <model_name> with your pulled model, e.g. dolphin-phi.

.. tabs::
   .. tab:: Linux

      .. code-block:: bash

         curl http://localhost:11434/api/generate -d '
         { 
            "model": "<model_name>", 
            "prompt": "Why is the sky blue?", 
            "stream": false
         }'

   .. tab:: Windows

      Please run the following command in Anaconda Prompt.

      .. code-block:: bash

         curl http://localhost:11434/api/generate -d "
         {
            \"model\": \"<model_name>\",
            \"prompt\": \"Why is the sky blue?\",
            \"stream\": false
         }"

Using Ollama Run GGUF models

Ollama supports importing GGUF models in the Modelfile, for example, suppose you have downloaded a mistral-7b-instruct-v0.1.Q4_K_M.gguf from Mistral-7B-Instruct-v0.1-GGUF, then you can create a file named Modelfile:

FROM ./mistral-7b-instruct-v0.1.Q4_K_M.gguf
TEMPLATE [INST] {{ .Prompt }} [/INST]
PARAMETER num_predict 64

Then you can create the model in Ollama by ollama create example -f Modelfile and use ollama run to run the model directly on console.

.. tabs::
   .. tab:: Linux

      .. code-block:: bash

         export no_proxy=localhost,127.0.0.1
         ./ollama create example -f Modelfile
         ./ollama run example

   .. tab:: Windows

      Please run the following command in Anaconda Prompt.

      .. code-block:: bash

         set no_proxy=localhost,127.0.0.1
         ollama create example -f Modelfile
         ollama run example

An example process of interacting with model with ollama run example looks like the following: