From 94aa182c5273670f0d223b794fee7eff37a6acbb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=AD=99=E6=B5=B7=E4=BA=AE?= <sunhailiang3@huawei.com>
Date: Thu, 26 Dec 2024 17:13:52 +0800
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diff --git "a/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md" "b/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
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@@ -0,0 +1,83 @@
+# CPU推理模型部署指南
+
+## 介绍
+
+sysInfer是一款基于鲲鹏+openEuler的大模型推理加速框架，主要采用通用算力CPU作为部署主体，目前支持使用Baichuan系列和Llama3系列的权重进行加速推理，支持使用gguf的量化格式模型。作为CPU的推理框架，突破了传统GPU部署大模型推理的限制，采用点到点的推理部署策略，充分挖掘通用算力的潜能，显著提升了CPU在大模型推理场景中的效率和价值。同时，该框架深化了鲲鹏处理器的特性支持，并与openEuler生态深度融合，助力拓展了鲲鹏和openEuler在大模型领域的生态应用广度。
+
+## 环境准备
+
+### 硬件要求
+
+| 类型           |     硬件要求                  |
+|----------------| -----------------------------|
+| 服务器         | 1台                           |
+| CPU           | 鲲鹏>= 64 cores   |
+| 内存         | >= 256 GB                 |
+| 内存带宽        | >= 100 GB/s               |
+
+### 权重模型
+
+支持huggingface上的Baichuan2-13B和Llama3-8B等开源权重模型
+
+```shell
+mkdir -r /home/app
+cd /home/app
+# 以Baichuan2-13B-Chat-GGUF为例
+wget https://hf-mirror.com/second-state/Baichuan2-13B-Chat-GGUF/resolve/main/Baichuan2-13B-Chat-Q4_0.gguf
+# Llama3
+wget https://hf-mirror.com/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-4bit/resolve/main/Llama3-8B-Chinese-Chat-q4_0-v2_1.gguf
+```
+
+### docker镜像拉取
+
+```shell
+# oe_openai_server:0.0.1镜像
+docker pull hub.oepkgs.net/neocopilot/oe_openai_server@sha256:2c87870fc34754391b1160bc0478f2691d0d47e47bb5a5bc18d8ec352d73fcb3
+```
+
+
+
+## 部署openai服务接口
+
+### 接口部署
+
+```shell
+# 查看鲲鹏920系列服务器逻辑核分布情况，下面以鲲鹏920为例
+lscpu
+```
+<img src="./picture/CPU逻辑核心.png" alt="CPU逻辑核心分布图" style="zoom: 200%;" />
+
+```shell
+# 以鲲鹏920为例进行绑核，逻辑核总数设置4的倍数，绑核按numa节点平均分配
+docker run -it \
+    -v /home/app/Baichuan2-13B-Chat-Q4_0.gguf:/app/Baichuan2-13B-Chat-Q4_0.gguf \
+    -p 7860:7860 \
+    -e OMP_NUM_THREADS=88 \
+    -e GOMP_CPU_AFFINITY=0-21,24-45,48-69,72-93 \
+    hub.oepkgs.net/neocopilot/oe_openai_server:0.0.1 \
+    ./server /app/Baichuan2-13B-Chat-Q4_0.gguf -t 0.0 -n 4096
+```
+
+### 接口功能测试
+
+按照openai官方文档构建的python脚本，用于验证功能，验证无误后可接入EulerCopilot。
+
+```python
+# request.py
+from openai import OpenAI
+client = OpenAI(base_url="http://127.0.0.1:7860/v1")
+
+completion = client.chat.completions.create(
+  model="Baichuan2-13B-Chat-Q4_0",
+  messages=[
+    {"role": "system", "content": "你是智能问答机器人。"},
+    {"role": "user", "content": "请你告诉我openEuler操作系统的发布规律。"}
+  ],
+  stream=True
+)
+
+for chunk in completion:
+  print(chunk.choices[0].delta)
+
+```
+
diff --git "a/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/picture/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png" "b/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/picture/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png"
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From 45cba05626f5df9a0485300d8dcd816c6a57eaba Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=AD=99=E6=B5=B7=E4=BA=AE?= <sunhailiang3@huawei.com>
Date: Thu, 26 Dec 2024 17:13:52 +0800
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---
 ...50\347\275\262\346\214\207\345\215\227.md" |  83 ++++++++++++++++++
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 create mode 100644 "docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
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diff --git "a/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md" "b/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
new file mode 100644
index 000000000..2a2b89cf0
--- /dev/null
+++ "b/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
@@ -0,0 +1,83 @@
+# CPU推理模型部署指南
+
+## 介绍
+
+sysInfer是一款基于鲲鹏+openEuler的大模型推理加速框架，主要采用通用算力CPU作为部署主体，目前支持使用Baichuan系列和Llama3系列的权重进行加速推理，支持使用gguf的量化格式模型。作为CPU的推理框架，突破了传统GPU部署大模型推理的限制，采用点到点的推理部署策略，充分挖掘通用算力的潜能，显著提升了CPU在大模型推理场景中的效率和价值。同时，该框架深化了鲲鹏处理器的特性支持，并与openEuler生态深度融合，助力拓展了鲲鹏和openEuler在大模型领域的生态应用广度。
+
+## 环境准备
+
+### 硬件要求
+
+| 类型           |     硬件要求                  |
+|----------------| -----------------------------|
+| 服务器         | 1台                           |
+| CPU           | 鲲鹏>= 64 cores   |
+| 内存         | >= 256 GB                 |
+| 内存带宽        | >= 100 GB/s               |
+
+### 权重模型
+
+支持huggingface上的Baichuan2-13B和Llama3-8B等开源权重模型
+
+```shell
+mkdir -r /home/app
+cd /home/app
+# 以Baichuan2-13B-Chat-GGUF为例
+wget https://hf-mirror.com/second-state/Baichuan2-13B-Chat-GGUF/resolve/main/Baichuan2-13B-Chat-Q4_0.gguf
+# Llama3
+wget https://hf-mirror.com/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-4bit/resolve/main/Llama3-8B-Chinese-Chat-q4_0-v2_1.gguf
+```
+
+### docker镜像拉取
+
+```shell
+# oe_openai_server:0.0.1镜像
+docker pull hub.oepkgs.net/neocopilot/oe_openai_server@sha256:2c87870fc34754391b1160bc0478f2691d0d47e47bb5a5bc18d8ec352d73fcb3
+```
+
+
+
+## 部署openai服务接口
+
+### 接口部署
+
+```shell
+# 查看鲲鹏920系列服务器逻辑核分布情况，下面以鲲鹏920为例
+lscpu
+```
+<img src="./pictures/CPU推理部署/CPU逻辑核心.png" alt="CPU逻辑核心分布图" style="zoom: 200%;" />
+
+```shell
+# 以鲲鹏920为例进行绑核，逻辑核总数设置4的倍数，绑核按numa节点平均分配
+docker run -it \
+    -v /home/app/Baichuan2-13B-Chat-Q4_0.gguf:/app/Baichuan2-13B-Chat-Q4_0.gguf \
+    -p 7860:7860 \
+    -e OMP_NUM_THREADS=88 \
+    -e GOMP_CPU_AFFINITY=0-21,24-45,48-69,72-93 \
+    hub.oepkgs.net/neocopilot/oe_openai_server:0.0.1 \
+    ./server /app/Baichuan2-13B-Chat-Q4_0.gguf -t 0.0 -n 4096
+```
+
+### 接口功能测试
+
+按照openai官方文档构建的python脚本，用于验证功能，验证无误后可接入EulerCopilot。
+
+```python
+# request.py
+from openai import OpenAI
+client = OpenAI(base_url="http://127.0.0.1:7860/v1")
+
+completion = client.chat.completions.create(
+  model="Baichuan2-13B-Chat-Q4_0",
+  messages=[
+    {"role": "system", "content": "你是智能问答机器人。"},
+    {"role": "user", "content": "请你告诉我openEuler操作系统的发布规律。"}
+  ],
+  stream=True
+)
+
+for chunk in completion:
+  print(chunk.choices[0].delta)
+
+```
+
diff --git "a/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/pictures/CPU\346\216\250\347\220\206\351\203\250\347\275\262/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png" "b/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/pictures/CPU\346\216\250\347\220\206\351\203\250\347\275\262/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png"
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Gitee


From 21b9030aebcc41bb1dbcfadb6540c1926c372e7b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=AD=99=E6=B5=B7=E4=BA=AE?= <sunhailiang3@huawei.com>
Date: Thu, 26 Dec 2024 19:16:29 +0800
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---
 ...50\347\275\262\346\214\207\345\215\227.md" |  83 ++++++++++++++++++
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 create mode 100644 "docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
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diff --git "a/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md" "b/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
new file mode 100644
index 000000000..2a2b89cf0
--- /dev/null
+++ "b/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
@@ -0,0 +1,83 @@
+# CPU推理模型部署指南
+
+## 介绍
+
+sysInfer是一款基于鲲鹏+openEuler的大模型推理加速框架，主要采用通用算力CPU作为部署主体，目前支持使用Baichuan系列和Llama3系列的权重进行加速推理，支持使用gguf的量化格式模型。作为CPU的推理框架，突破了传统GPU部署大模型推理的限制，采用点到点的推理部署策略，充分挖掘通用算力的潜能，显著提升了CPU在大模型推理场景中的效率和价值。同时，该框架深化了鲲鹏处理器的特性支持，并与openEuler生态深度融合，助力拓展了鲲鹏和openEuler在大模型领域的生态应用广度。
+
+## 环境准备
+
+### 硬件要求
+
+| 类型           |     硬件要求                  |
+|----------------| -----------------------------|
+| 服务器         | 1台                           |
+| CPU           | 鲲鹏>= 64 cores   |
+| 内存         | >= 256 GB                 |
+| 内存带宽        | >= 100 GB/s               |
+
+### 权重模型
+
+支持huggingface上的Baichuan2-13B和Llama3-8B等开源权重模型
+
+```shell
+mkdir -r /home/app
+cd /home/app
+# 以Baichuan2-13B-Chat-GGUF为例
+wget https://hf-mirror.com/second-state/Baichuan2-13B-Chat-GGUF/resolve/main/Baichuan2-13B-Chat-Q4_0.gguf
+# Llama3
+wget https://hf-mirror.com/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-4bit/resolve/main/Llama3-8B-Chinese-Chat-q4_0-v2_1.gguf
+```
+
+### docker镜像拉取
+
+```shell
+# oe_openai_server:0.0.1镜像
+docker pull hub.oepkgs.net/neocopilot/oe_openai_server@sha256:2c87870fc34754391b1160bc0478f2691d0d47e47bb5a5bc18d8ec352d73fcb3
+```
+
+
+
+## 部署openai服务接口
+
+### 接口部署
+
+```shell
+# 查看鲲鹏920系列服务器逻辑核分布情况，下面以鲲鹏920为例
+lscpu
+```
+<img src="./pictures/CPU推理部署/CPU逻辑核心.png" alt="CPU逻辑核心分布图" style="zoom: 200%;" />
+
+```shell
+# 以鲲鹏920为例进行绑核，逻辑核总数设置4的倍数，绑核按numa节点平均分配
+docker run -it \
+    -v /home/app/Baichuan2-13B-Chat-Q4_0.gguf:/app/Baichuan2-13B-Chat-Q4_0.gguf \
+    -p 7860:7860 \
+    -e OMP_NUM_THREADS=88 \
+    -e GOMP_CPU_AFFINITY=0-21,24-45,48-69,72-93 \
+    hub.oepkgs.net/neocopilot/oe_openai_server:0.0.1 \
+    ./server /app/Baichuan2-13B-Chat-Q4_0.gguf -t 0.0 -n 4096
+```
+
+### 接口功能测试
+
+按照openai官方文档构建的python脚本，用于验证功能，验证无误后可接入EulerCopilot。
+
+```python
+# request.py
+from openai import OpenAI
+client = OpenAI(base_url="http://127.0.0.1:7860/v1")
+
+completion = client.chat.completions.create(
+  model="Baichuan2-13B-Chat-Q4_0",
+  messages=[
+    {"role": "system", "content": "你是智能问答机器人。"},
+    {"role": "user", "content": "请你告诉我openEuler操作系统的发布规律。"}
+  ],
+  stream=True
+)
+
+for chunk in completion:
+  print(chunk.choices[0].delta)
+
+```
+
diff --git "a/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/pictures/CPU\346\216\250\347\220\206\351\203\250\347\275\262/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png" "b/docs/user-guide/\351\203\250\347\275\262\346\214\207\345\215\227/pictures/CPU\346\216\250\347\220\206\351\203\250\347\275\262/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png"
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-- 
Gitee


From 0d59cb58c85314ec6ee78d63081a0ba35b2f5b2c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=AD=99=E6=B5=B7=E4=BA=AE?= <sunhailiang3@huawei.com>
Date: Thu, 26 Dec 2024 11:18:36 +0000
Subject: [PATCH 4/4] Revert "Merge branch 'master' of
 https://gitee.com/qmzznbxhl/euler-copilot-framework_1"

This reverts commit 2fa8098efe9a4283fb819fd4f449568e49daa257.
---
 ...50\347\275\262\346\214\207\345\215\227.md" |  83 ------------------
 ...3\350\276\221\346\240\270\345\277\203.png" | Bin 6950 -> 0 bytes
 2 files changed, 83 deletions(-)
 delete mode 100644 "docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
 delete mode 100644 "docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/picture/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png"

diff --git "a/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md" "b/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
deleted file mode 100644
index 2a0fae41c..000000000
--- "a/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/CPU\346\216\250\347\220\206\346\250\241\345\236\213\351\203\250\347\275\262\346\214\207\345\215\227.md"
+++ /dev/null
@@ -1,83 +0,0 @@
-# CPU推理模型部署指南
-
-## 介绍
-
-sysInfer是一款基于鲲鹏+openEuler的大模型推理加速框架，主要采用通用算力CPU作为部署主体，目前支持使用Baichuan系列和Llama3系列的权重进行加速推理，支持使用gguf的量化格式模型。作为CPU的推理框架，突破了传统GPU部署大模型推理的限制，采用点到点的推理部署策略，充分挖掘通用算力的潜能，显著提升了CPU在大模型推理场景中的效率和价值。同时，该框架深化了鲲鹏处理器的特性支持，并与openEuler生态深度融合，助力拓展了鲲鹏和openEuler在大模型领域的生态应用广度。
-
-## 环境准备
-
-### 硬件要求
-
-| 类型           |     硬件要求                  |
-|----------------| -----------------------------|
-| 服务器         | 1台                           |
-| CPU           | 鲲鹏>= 64 cores   |
-| 内存         | >= 256 GB                 |
-| 内存带宽        | >= 100 GB/s               |
-
-### 权重模型
-
-支持huggingface上的Baichuan2-13B和Llama3-8B等开源权重模型
-
-```shell
-mkdir -r /home/app
-cd /home/app
-# 以Baichuan2-13B-Chat-GGUF为例
-wget https://hf-mirror.com/second-state/Baichuan2-13B-Chat-GGUF/resolve/main/Baichuan2-13B-Chat-Q4_0.gguf
-# Llama3
-wget https://hf-mirror.com/shenzhi-wang/Llama3-8B-Chinese-Chat-GGUF-4bit/resolve/main/Llama3-8B-Chinese-Chat-q4_0-v2_1.gguf
-```
-
-### docker镜像拉取
-
-```shell
-# oe_openai_server:0.0.1镜像
-docker pull hub.oepkgs.net/neocopilot/oe_openai_server@sha256:2c87870fc34754391b1160bc0478f2691d0d47e47bb5a5bc18d8ec352d73fcb3
-```
-
-
-
-## 部署openai服务接口
-
-### 接口部署
-
-```shell
-# 查看鲲鹏920系列服务器逻辑核分布情况，下面以鲲鹏920为例
-lscpu
-```
-<img src="./picture/CPU逻辑核心.png" alt="CPU逻辑核心分布图" style="zoom: 200%;" />
-
-```shell
-# 以鲲鹏920为例进行绑核，逻辑核总数设置4的倍数，绑核按numa节点平均分配
-docker run -it \
-    -v /home/app/Baichuan2-13B-Chat-Q4_0.gguf:/app/Baichuan2-13B-Chat-Q4_0.gguf \
-    -p 7860:7860 \
-    -e OMP_NUM_THREADS=88 \
-    -e GOMP_CPU_AFFINITY=0-21,24-45,48-69,72-93 \
-    hub.oepkgs.net/neocopilot/oe_openai_server:0.0.1 \
-    ./server /app/Baichuan2-13B-Chat-Q4_0.gguf -t 0.0 -n 4096
-```
-
-### 接口功能测试
-
-按照openai官方文档构建的python脚本，用于验证功能，验证无误后可接入EulerCopilot。
-
-```python
-# request.py
-from openai import OpenAI
-client = OpenAI(base_url="http://127.0.0.1:7860/v1")
-
-completion = client.chat.completions.create(
-  model="Baichuan2-13B-Chat-Q4_0",
-  messages=[
-    {"role": "system", "content": "你是智能问答机器人。"},
-    {"role": "user", "content": "请你告诉我openEuler操作系统的发布规律。"}
-  ],
-  stream=True
-)
-
-for chunk in completion:
-  print(chunk.choices[0].delta)
-
-```
-
diff --git "a/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/picture/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png" "b/docs/user-guide/CPU\351\203\250\347\275\262\346\214\207\345\215\227/picture/CPU\351\200\273\350\276\221\346\240\270\345\277\203.png"
deleted file mode 100644
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literal 0
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DIFzr!

-- 
Gitee