From d963e95363d04e2a1d0af1ada4eb3ef24aaa9264 Mon Sep 17 00:00:00 2001
From: Xiangyu Tian <109123695+xiangyuT@users.noreply.github.com>
Date: Fri, 17 May 2024 10:14:00 +0800
Subject: [PATCH] LLM: Modify CPU Installation Command for documentation
 (#11042)

* init

* refine

* refine

* refine

* refine comments
---
 .../source/doc/LLM/Overview/install_cpu.md    | 39 ++++++++++++++++---
 .../Model/baichuan/README.md                  | 15 ++++++-
 2 files changed, 46 insertions(+), 8 deletions(-)

diff --git a/docs/readthedocs/source/doc/LLM/Overview/install_cpu.md b/docs/readthedocs/source/doc/LLM/Overview/install_cpu.md
index 53342b77..cbe3ce83 100644
--- a/docs/readthedocs/source/doc/LLM/Overview/install_cpu.md
+++ b/docs/readthedocs/source/doc/LLM/Overview/install_cpu.md
@@ -4,8 +4,20 @@
 
 Install IPEX-LLM for CPU supports using pip through:
 
-```bash
-pip install --pre --upgrade ipex-llm[all] # install the latest ipex-llm nightly build with 'all' option
+```eval_rst	
+.. tabs::
+
+   .. tab:: Linux
+
+      .. code-block:: bash
+
+         pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
+
+   .. tab:: Windows
+
+      .. code-block:: cmd
+
+         pip install --pre --upgrade ipex-llm[all]
 ```
 
 Please refer to [Environment Setup](#environment-setup) for more information.
@@ -41,11 +53,26 @@ For optimal performance with LLM models using IPEX-LLM optimizations on Intel CP
 
 First we recommend using [Conda](https://docs.conda.io/en/latest/miniconda.html) to create a python 3.11 enviroment:
 
-```bash
-conda create -n llm python=3.11
-conda activate llm
+```eval_rst	
+.. tabs::
 
-pip install --pre --upgrade ipex-llm[all] # install the latest ipex-llm nightly build with 'all' option
+   .. tab:: Linux
+
+      .. code-block:: bash
+
+         conda create -n llm python=3.11
+         conda activate llm
+
+         pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu
+
+   .. tab:: Windows
+
+      .. code-block:: cmd
+
+         conda create -n llm python=3.11
+         conda activate llm
+
+         pip install --pre --upgrade ipex-llm[all]
 ```
 
 Then for running a LLM model with IPEX-LLM optimizations (taking an `example.py` an example):
diff --git a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan/README.md b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan/README.md
index 6b8d421d..c6aee4d5 100644
--- a/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan/README.md
+++ b/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan/README.md
@@ -8,14 +8,25 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 In the example [generate.py](./generate.py), we show a basic use case for a Baichuan model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 1. Install
 We suggest using conda to manage environment:
+
+On Linux:
 ```bash
 conda create -n llm python=3.11
 conda activate llm
 
-pip install ipex-llm[all] # install ipex-llm with 'all' option
+pip install --pre --upgrade ipex-llm[all] --extra-index-url https://download.pytorch.org/whl/cpu # install ipex-llm with 'all' option
 pip install transformers_stream_generator  # additional package required for Baichuan-13B-Chat to conduct generation
 ```
 
+On Windows:
+```cmd
+conda create -n llm python=3.11
+conda activate llm
+
+pip install --pre --upgrade ipex-llm[all]
+pip install transformers_stream_generator
+```
+
 ### 2. Run
 ```
 python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT
@@ -32,7 +43,7 @@ Arguments info:
 
 #### 2.1 Client
 On client Windows machine, it is recommended to run directly with full utilization of all cores:
-```powershell
+```cmd
 python ./generate.py 
 ```