Update Dockerfile, LICENSE.txt, requirements.txt, model.properties, README.md, whl.zip files

Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+COPY requirements.txt requirements.txt
+RUN source /opt/dtk-24.04/env.sh
+ENV LANG C.UTF-8
+RUN pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
+

LICENSE.txt

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README.md

+# Yi-1.5-6B
+
+## 论文
+
+`Yi: Open Foundation Models by 01.AI`
+
+- [https://arxiv.org/abs/2403.04652]
+
+## 算法原理
+
+Yi-1.5-6B 是一个 decoder-only 的 transformer 模型，使用 SwiGLU激活函数、GQA、RoPE等
+
+
+## 环境配置
+
+### Docker（方法一）
+
+此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取 docker 镜像的地址与使用步骤
+
+```
+docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-centos7.6-dtk24.04-py310
+docker run -it --shm-size=1024G -v /parastor/home/Yi-1.5-pytorch:/home/Yi-1.5-pytorch -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name Yi-1.5  <your IMAGE ID> bash  # <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：c85ed27005f2
+cd /home/Yi-1.5-pytorch
+pip install -e .[torch,metrics]
+# deepspeed、bitsandbytes可从whl.zip文件里获取安装：
+pip install deepspeed-0.12.3+das1.0+gita724046.abi0.dtk2404.torch2.1.0-cp310-cp310-manylinux2014_x86_64.whl
+pip install bitsandbytes-0.42.0-py3-none-any.whl
+
+```
+
+### Dockerfile（方法二）
+
+此处提供 dockerfile 的使用方法
+
+```
+docker build  -t Yi-1.5-df:latest .
+docker run -it --shm-size=1024G -v /parastor/home/Yi-1.5-pytorch:/home/Yi-1.5-pytorch -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name Yi-1.5  Yi-1.5-df  bash
+pip install -r requirement.txt -i https://mirrors.aliyun.com/pypi/simple/  --trusted-host mirrors.aliyun.com
+# deepspeed、bitsandbytes可从whl.zip文件里获取安装：
+pip install deepspeed-0.12.3+das1.0+gita724046.abi0.dtk2404.torch2.1.0-cp310-cp310-manylinux2014_x86_64.whl
+pip install bitsandbytes-0.42.0-py3-none-any.whl
+
+```
+
+### Anaconda（方法三）
+
+此处提供本地配置、编译的详细步骤，例如：
+
+关于本项目 DCU 显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
+
+```
+DTK驱动：dtk23.04
+python：python3.10
+torch: 2.1.0
+torchvision: 0.16.0
+deepspeed:0.12.3
+bitsandbytes: 0.42.0
+triton:2.1.0
+```
+
+`Tips：以上dtk驱动、python、paddle等DCU相关工具版本需要严格一一对应`
+
+其它非深度学习库参照 requirements.txt 安装：
+
+```
+pip install -r requirements.txt
+```
+
+## 数据集
+使用alpaca_en.json数据集，已经包含在data目录中，具体文件为alpaca_en_demo.json
+项目中已提供用于试验训练的迷你数据集，训练数据目录结构如下，用于正常训练的完整数据集请按此目录结构进行制备：
+
+```
+ ── data
+    ├── alpaca_en_demo.json.json
+    └── alpaca_zh_demo.json.json
+```
+
+## 训练
+
+一般情况下，ModelZoo 上的项目提供单机训练的启动方法即可，单机单卡、单机多卡至少提供其一训练方法。
+
+### 单机单卡
+
+```
+cd fintune
+sh single_dcu_finetune_lora.sh
+```
+### 单机多卡
+
+```
+sh multi_node.sh
+```
+
+
+## 推理
+
+```
+cd examples/inference
+sh single_node.sh
+```
+
+## result
+
+使用的加速卡:2张 DCU-K100-64G
+
+<div align=center>
+    <img src="./doc/training_loss.png"/>
+</div>
+
+### 精度
+测试数据：[alpaca_en_demo.json]，使用的加速卡:K100-64G,2卡训练。
+
+根据测试结果情况填写表格：
+| device | train_loss | 
+| :------: | :------: | 
+| DCU-K100 | 0.7647 | 
+| GPU-A800 | 0.7651 | 
+
+## 应用场景
+
+### 算法类别
+
+对话问答
+
+### 热点应用行业
+
+`科研,教育,政府,金融`
+
+
+## 源码仓库及问题反馈
+
+- https://github.com/01-ai/Yi-1.5
+
+## 参考资料
+
+- https://github.com/hiyouga/LLaMA-Factory/tree/main
+- https://github.com/01-ai/Yi-1.5
+- (https://huggingface.co/01-ai)
+

model.properties

+# 模型唯一标识
+modelCode=738
+# 模型名称
+modelName=Yi_1.5_6B_chat
+# 模型描述
+modelDescription=Yi-1.5-6B是由零一万物公司开发并开源的一系列人工智能模型中的一种，属于 Yi-1.5 系列模型，是原先 Yi 模型的升级版本。
+# 应用场景
+appScenario=推理,训练,对话问答,科研,教育,政府,金融
+# 框架类型
+frameType=Pytorch

requirements.txt

+transformers>=4.37.2
+datasets>=2.14.3
+accelerate>=0.27.2
+peft>=0.10.0
+trl>=0.8.1
+gradio>=4.0.0
+scipy
+einops
+sentencepiece
+protobuf
+uvicorn
+pydantic
+fastapi
+sse-starlette
+matplotlib>=3.7.0
+fire
+packaging
+pyyaml
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