add README for llm-inference-cpu (#9147)

* add README for llm-inference-cpu

* modify README

* add README for llm-inference-cpu on Windows

docker/llm/README.md

@@ -0,0 +1,90 @@
+## Getting started with BigDL LLM on Windows
+
+### Install docker
+
+New users can quickly get started with Docker using this [official link](https://www.docker.com/get-started/).
+
+For Windows users, make sure Hyper-V is enabled on your computer. The instructions for installing on Windows can be accessed from [here](https://docs.docker.com/desktop/install/windows-install/).
+
+
+### Pull bigdl-llm-cpu image
+
+To pull image from hub, you can execute command on console:
+```powershell
+docker pull intelanalytics/bigdl-llm-cpu:2.4.0-SNAPSHOT
+```
+to check if the image is successfully downloaded, you can use:
+```powershell
+docker images | sls intelanalytics/bigdl-llm-cpu:2.4.0-SNAPSHOT
+```
+
+
+### Start bigdl-llm-cpu container
+
+To run the image and do inference, you could create and run a bat script on Windows.
+
+An example could be:
+```bat
+@echo off
+set DOCKER_IMAGE=intelanalytics/bigdl-llm-cpu:2.4.0-SNAPSHOT
+set CONTAINER_NAME=my_container
+set MODEL_PATH=D:/llm/models[change to your model path]
+
+:: Run the Docker container
+docker run -itd ^
+    --net=host ^
+    --cpuset-cpus="0-7" ^
+    --cpuset-mems="0" ^
+    --memory="8G" ^
+    --name=%CONTAINER_NAME% ^
+    -v %MODEL_PATH%:/llm/models ^
+    %DOCKER_IMAGE%
+```
+
+After the container is booted, you could get into the container through `docker exec`.
+```
+docker exec -it my_container bash
+```
+
+To run inference using `BigDL-LLM` using cpu, you could refer to this [documentation](https://github.com/intel-analytics/BigDL/tree/main/python/llm#cpu-int4).
+
+
+### Getting started with chat
+
+chat.py can be used to initiate a conversation with a specified model. The file is under directory '/llm'.
+
+You can download models and bind the model directory from host machine to container when start a container.
+
+After entering the container through `docker exec`, you can run chat.py by:
+```bash
+cd /llm
+python chat.py --model-path YOUR_MODEL_PATH
+```
+If your model is chatglm-6b and mounted on /llm/models, you can excute:
+```bash
+python chat.py --model-path /llm/models/chatglm-6b
+```
+Here is a demostration:
+<p align="left">
+            <img src="https://llm-assets.readthedocs.io/en/latest/_images/llm-inference-cpu-docker-chatpy-demo.gif" width='60%' /> 
+
+</p>
+
+### Getting started with tutorials
+
+You could start a jupyter-lab serving to explore bigdl-llm-tutorial which can help you build a more sophisticated Chatbo.
+
+To start serving,  run the script under '/llm':
+```bash
+cd /llm
+./start-notebook.sh [--port EXPECTED_PORT]
+```
+You could assign a port to serving, or the default port 12345 will be assigned.
+
+If you use host network mode when booted the container, after successfully running service, you can access http://127.0.0.1:12345/lab to get into tutorial, or you should bind the correct ports between container and host. 
+
+Here is a demostration of how to use tutorial in explorer:
+<p align="left">
+            <img src="https://llm-assets.readthedocs.io/en/latest/_images/llm-inference-cpu-docker-tutorial-demo.gif" width='60%' /> 
+
+</p>