Update ollama quickstart (#10756)

* update windows part

* update ollama quickstart

* update ollama

* update

* small fix

* update

* meet review

docs/readthedocs/source/doc/LLM/Quickstart/llama_cpp_quickstart.md

@@ -15,7 +15,7 @@ IPEX-LLM's support for `llama.cpp` now is avaliable for Linux system and Windows
 #### Linux
 For Linux system, we recommend Ubuntu 20.04 or later (Ubuntu 22.04 is preferred).
 
-Visit the [Install IPEX-LLM on Linux with Intel GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html), follow [Install Intel GPU Driver](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html#install-intel-gpu-driver) and [Install oneAPI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html#install-oneapi) to install GPU driver and [Intel® oneAPI Base Toolkit 2024.0](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html).
+Visit the [Install IPEX-LLM on Linux with Intel GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html), follow [Install Intel GPU Driver](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html#install-intel-gpu-driver) and [Install oneAPI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_linux_gpu.html#install-oneapi) to install GPU driver and Intel® oneAPI Base Toolkit 2024.0.
 
 #### Windows
 Visit the [Install IPEX-LLM on Windows with Intel GPU Guide](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html), and follow [Install Prerequisites](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/install_windows_gpu.html#install-prerequisites) to install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/) Community Edition, latest [GPU driver](https://www.intel.com/content/www/us/en/download/785597/intel-arc-iris-xe-graphics-windows.html) and Intel® oneAPI Base Toolkit 2024.0.

docs/readthedocs/source/doc/LLM/Quickstart/ollama_quickstart.md

@@ -1,51 +1,81 @@
-# Run Ollama on Linux with Intel GPU
+# 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)*.
 
-```eval_rst
-.. note::
-   Only Linux is currently supported.
-```
-
 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 with Ollama Binaries
+### 1 Install IPEX-LLM for Ollama
 
-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 [Install Prerequisits on Linux](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html#linux) , 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. 
+IPEX-LLM's support for `ollama` now is avaliable for Linux system and Windows system.
 
-**After the installation, you should have created a conda environment, named `llm-cpp` for instance, for running `llama.cpp` commands with IPEX-LLM.**
+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.
 
-```bash
+```eval_rst
-conda activate llm-cpp
+.. tabs::
-init-ollama
+   .. 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
 
 
 Launch the Ollama service:
 
-```bash
+```eval_rst
-conda activate llm-cpp
+.. tabs::
+   .. tab:: Linux
 
-export no_proxy=localhost,127.0.0.1
+      .. code-block:: bash
-export ZES_ENABLE_SYSMAN=1
+
-source /opt/intel/oneapi/setvars.sh
+         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 no_proxy=localhost,127.0.0.1
+         set ZES_ENABLE_SYSMAN=1
+         call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat"
+
+         ollama.exe serve
 
-./ollama serve
 ```
 
 ```eval_rst
 .. 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`.
 ```
 
@@ -56,55 +86,101 @@ The console will display messages similar to the following:
 </a>
 
 
-
 ### 4 Pull Model
-Keep the Ollama service on and open another terminal and run `./ollama pull <model_name>` to automatically pull a model. e.g. `dolphin-phi:latest`:
+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`.
+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,
+            "options":{"num_gpu": 999}
+         }'
+
+   .. 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,
+            \"options\":{\"num_gpu\": 999}
+         }"
 
-```shell
-curl http://localhost:11434/api/generate -d '
-{ 
-  "model": "<model_name>", 
-  "prompt": "Why is the sky blue?", 
-  "stream": false 
-}'
 ```
 
-An example output of using model `doplphin-phi` looks like the following:
 
-<a href="https://llm-assets.readthedocs.io/en/latest/_images/ollama_curl.png" target="_blank">
+```eval_rst
-  <img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_curl.png" width=100%; />
+.. note::
-</a>
 
+  Please don't forget to set ``"options":{"num_gpu": 999}`` to make sure all layers of your model are running on Intel GPU, otherwise, some layers may run on CPU.
+```
 
-#### Using Ollama Run
+#### Using Ollama Run GGUF models
-
-You can also use `ollama run` to run the model directly on console. **Replace the <model_name> with your pulled model**, e.g. `dolphin-phi`. This command will seamlessly download, load the model, and enable you to interact with it through a streaming conversation."
 
+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
-conda activate llm-cpp
+FROM ./mistral-7b-instruct-v0.1.Q4_K_M.gguf
-
+TEMPLATE [INST] {{ .Prompt }} [/INST]
-export no_proxy=localhost,127.0.0.1
+PARAMETER num_gpu 999
-export ZES_ENABLE_SYSMAN=1
+PARAMETER num_predict 64
-source /opt/intel/oneapi/setvars.sh
-
-./ollama run <model_name>
 ```
 
-An example process of interacting with model with `ollama run` looks like the following:
+```eval_rst
+.. note::
 
-<a href="https://llm-assets.readthedocs.io/en/latest/_images/ollama_run_1.png" target="_blank">
+  Please don't forget to set ``PARAMETER num_gpu 999`` to make sure all layers of your model are running on Intel GPU, otherwise, some layers may run on CPU.
-  <img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_run_1.png" width=100%; /><img src="https://llm-assets.readthedocs.io/en/latest/_images/ollama_run_2.png" width=100%; />
+```
+
+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.exe create example -f Modelfile
+         ollama.exe 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>