# Run Ollama with IPEX-LLM on Intel GPU

[ollama/ollama](https://github.com/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`](https://github.com/intel-analytics/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.

<video src="https://llm-assets.readthedocs.io/en/latest/_images/ollama-linux-arc.mp4" width="100%" controls></video>

## Quickstart

### 1 Install IPEX-LLM for Ollama

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

Visit [Run llama.cpp with IPEX-LLM on Intel GPU Guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html), and follow the instructions in section [Prerequisites](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html#prerequisites) to setup and section [Install IPEX-LLM cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html#install-ipex-llm-for-llama-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.

```eval_rst
.. 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:

```eval_rst
.. 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

         ./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
         call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"

         ollama serve

```

```eval_rst
.. 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.
```

.. 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:

<a href="https://llm-assets.readthedocs.io/en/latest/_images/ollama_serve.png" target="_blank">
  <img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_serve.png" width=100%; />
</a>


### 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`:

<a href="https://llm-assets.readthedocs.io/en/latest/_images/ollama_pull.png" target="_blank">
  <img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_pull.png" width=100%; />
</a>


### 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`.

```eval_rst
.. 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](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/tree/main), then you can create a file named `Modelfile`:

```bash
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.

```eval_rst
.. 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:

<a href="https://llm-assets.readthedocs.io/en/latest/_images/ollama_gguf_demo_image.png" target="_blank">
  <img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_gguf_demo_image.png" width=100%; />
</a>