XiXi_Qwen_base_14b

XiXiLM

<div align="center"> <img src="https://github.com/AI4Bread/GouMang/blob/main/assets/goumang_logoallnew.png?raw=true" width="600"/> <div>&nbsp;</div> <div align="center"> <!-- <b><font size="5">XiXiLM</font></b> --> ^{<a href="http://www.ai4bread.com"> </a>} <div>&nbsp;</div> </div>

💻Github Repo • 🤔Reporting Issues • 📜Technical Report

</div> <p align="center"> 👋 join us on <a href="https://github.com/AI4Bread/GouMang" target="_blank">Github</a> </p>

Introduction

XiXiLM（GouMang LLM） has open-sourced a 7 billion parameter base model and a chat model tailored for agricultural scenarios. The model has the following characteristics:

1. High Professionalism: XiXiLM focuses on the agricultural field, providing professional and accurate answers especially in areas such as tuber crop cultivation, pest and disease control, and soil management.

2. Academic Support: The model is based on the latest agricultural research findings, capable of providing academic-level answers to help researchers and agricultural practitioners gain a deeper understanding of agricultural issues.

3. Multilingual Support: Supports both Chinese and English languages, making it convenient for users both domestically and internationally.

4. Free Commercial Use: The model weights are fully open, supporting not only academic research but also allowing free commercial usage. Users can use the model in commercial projects for free, lowering the usage threshold.

5. Efficient Training: Employs advanced training algorithms and techniques, enabling the model to respond quickly to user inquiries and provide efficient Q&A services.

6. Continuous Optimization: The model will be continuously optimized based on user feedback and the latest research findings, constantly improving the quality and coverage of its answers.

XiXiLM-Qwen-14B

Limitations: Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.

Import from Transformers

To load the XiXiLM model using Transformers, use the following code:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AI4Bread/XiXi_Qwen_base_14b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("AI4Bread/XiXi_Qwen_base_14b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])
print(response)
# Hello! How can I help you today?
response, history = model.chat(tokenizer, "马铃薯育种有什么注意事项？需要注意什么呢？", history=history)
print(response)

The responses can be streamed using stream_chat:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "AI4Bread/XiXi_Qwen_base_14b"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
length = 0
for response, history in model.stream_chat(tokenizer, "Hello", history=[]):
    print(response[length:], flush=True, end="")
    length = len(response)

Deployment

LMDeploy

LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams.

pip install lmdeploy

Or you can launch an OpenAI compatible server with the following command:

lmdeploy serve api_server internlm/internlm2-chat-7b --model-name internlm2-chat-7b --server-port 23333 

Then you can send a chat request to the server:

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-7b",
    "messages": [
    {"role": "system", "content": "你是一个专业的农业专家"},
    {"role": "user", "content": "马铃薯种植的时候有哪些注意事项？"}
    ]
    }'

The output be like:

Find more details in the LMDeploy documentation

vLLM

Launch OpenAI compatible server with vLLM>=0.3.2:

pip install vllm

python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-7b --served-model-name internlm2-chat-7b --trust-remote-code

Then you can send a chat request to the server:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-7b",
    "messages": [
    {"role": "system", "content": "You are a professional agriculture expert."},
    {"role": "user", "content": "Introduce potato farming to me."}
    ]
    }'

Find more details in the vLLM documentation

Used local trained model

First: Convert lmdeploy TurboMind

Here, we will use our pre-trained model file and execute the conversion in the user's root directory, as shown below.

# Converting Model to TurboMind (FastTransformer Format)
lmdeploy convert internlm2-chat-7b /root/autodl-tmp/agri_intern/XiXiLM --tokenizer-path ./GouMang/tokenizer.json

After execution, a workspace folder will be generated in the current directory. This folder contains the necessary files for TurboMind and Triton "Model Inference." as shown below:

Second: Chat Locally

lmdeploy chat turbomind ./workspace

Third(Optional): TurboMind Inference + API Service

In the previous section, we tried starting the Client directly using the command line. Now, we will attempt to use lmdeploy for service deployment.

The "Model Inference/Service" currently offers two service deployment methods: TurboMind and TritonServer. In this case, the Server is either TurboMind or TritonServer, and the API Server can provide external API services. We recommend using TurboMind.

First, start the service with the following command:

# ApiServer+Turbomind   api_server => AsyncEngine => TurboMind
lmdeploy serve api_server ./workspace \
	--server-name 0.0.0.0 \
	--server-port 23333 \
	--tp 1

In the above parameters, server_name and server_port indicate the service address and port, respectively. The tp parameter, as mentioned earlier, stands for Tensor Parallelism.

After this, users can start the Web Service as described in TurboMind Service as the Backend.

Web Service Startup Method 1:

Starting the Service with Gradio

This section demonstrates using Gradio as a front-end demo.

Since Gradio requires local access to display the interface, you also need to forward the data to your local machine via SSH. The command is as follows:

ssh -CNg -L 6006:127.0.0.1:6006 [email protected] -p <your ssh port>

--TurboMind Service as the Backend

The API Server is started the same way as in the previous section. Here, we directly start Gradio as the front-end.

# Gradio+ApiServer. The Server must be started first, and Gradio acts as the Client
lmdeploy serve gradio http://0.0.0.0:23333 --server-port 6006

--Other way(Recommended!!!)

Of course, Gradio can also connect directly with TurboMind, as shown below

# Gradio+Turbomind(local)
lmdeploy serve gradio ./workspace

You can start Gradio directly. In this case, there is no API Server, and TurboMind communicates directly with Gradio.

Web Service Startup Method 2:

Starting the Service with Streamlit

pip install streamlit==1.24.0

Download the GouMang project model (please Star if you like it)

git clone https://github.com/AI4Bread/GouMang.git
cd GouMang

Replace the model path in web_demo.py with the path where the downloaded parameters of GouMang are stored

Run the web_demo.py file in the directory, and after entering the following command, check this tutorial 5.2 for local port configuration，to map the port to your local machine. Enter http://127.0.0.1:6006 in your local browser.

streamlit run web_demo.py --server.address 127.0.0.1 --server.port 6006

Note: The model will load only after you open the http://127.0.0.1:6006 page in your browser. Once the model is loaded, you can start conversing with GouMang like this.

Open Source License

The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the <a href="https://wj.qq.com/s2/14897739/e871/" target="_blank">申请表（中文）</a>. For other questions or collaborations, please contact [email protected].

Citation

简介

XiXiLM ，即西西大模型（又名：句芒大模型），开源了面向农业问答的大模型。模型具有以下特点：

1. 专业性强：XiXiLM 专注于农业领域，特别是薯类作物的种植、病虫害防治、土壤管理等方面，提供专业、精准的解答。

2. 学术化支持：模型基于最新的农业研究成果，能够提供学术化的回答，帮助研究人员和农业从业者深入理解农业问题。

3. 多语言支持：支持中文和英文两种语言，方便国内外用户使用。

4. 免费商业使用：模型权重完全开放，不仅支持学术研究，还允许申请商业使用。用户可以在商业项目中免费使用该模型，降低了使用门槛。

5. 高效训练：采用先进的训练算法和技术，使得模型能够快速响应用户提问，提供高效的问答服务。

6. 持续优化：模型会根据用户反馈和最新研究成果进行持续优化，不断提升问答质量和覆盖面。

XiXiLM-Qwen-14B

局限性： 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。

通过 Transformers 加载

通过以下的代码加载 InternLM2 7B Chat 模型

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AI4Bread/XiXi_Qwen_base_14b", trust_remote_code=True)
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error.
model = AutoModelForCausalLM.from_pretrained("AI4Bread/XiXi_Qwen_base_14b", torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])
print(response)
# Hello! How can I help you today?
response, history = model.chat(tokenizer, "马铃薯育种有什么注意事项？需要注意什么呢？", history=history)
print(response)

如果想进行流式生成，则可以使用 stream_chat 接口：

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "AI4Bread/XiXi_Qwen_base_14b"
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

model = model.eval()
length = 0
for response, history in model.stream_chat(tokenizer, "马铃薯育种有什么注意事项？需要注意什么呢？", history=[]):
    print(response[length:], flush=True, end="")
    length = len(response)

部署

LMDeploy

LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。

pip install lmdeploy

你可以使用以下命令启动兼容 OpenAI API 的服务:

lmdeploy serve api_server internlm/internlm2-chat-7b --server-port 23333

然后你可以向服务端发起一个聊天请求:

curl http://localhost:23333/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-7b",
    "messages": [
    {"role": "system", "content": "你是一个专业的农业专家"},
    {"role": "user", "content": "马铃薯种植的时候有哪些注意事项？"}
    ]
    }'

更多信息请查看 LMDeploy 文档

vLLM

使用vLLM>=0.3.2启动兼容 OpenAI API 的服务:

pip install vllm

python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-7b --trust-remote-code

然后你可以向服务端发起一个聊天请求:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
    "model": "internlm2-chat-7b",
    "messages": [
    {"role": "system", "content": "你是一个专业的农业专家."},
    {"role": "user", "content": "请给我介绍一下马铃薯育种."}
    ]
    }'

更多信息请查看 vLLM 文档

使用本地训练模型

第一步：转换为 lmdeploy TurboMind 格式

这里，我们将使用预训练的模型文件，并在用户的根目录下执行转换，如下所示。

# 将模型转换为 TurboMind (FastTransformer 格式)
lmdeploy convert internlm2-chat-7b /root/autodl-tmp/agri_intern/XiXiLM --tokenizer-path ./GouMang/tokenizer.json

执行完毕后，当前目录下将生成一个 workspace 文件夹。 这个文件夹包含 TurboMind 和 Triton “模型推理”所需的文件，如下所示：

第二步：本地聊天

lmdeploy chat turbomind ./workspace

第三步（可选）：TurboMind 推理 + API 服务

在前一部分中，我们尝试通过命令行直接启动客户端。现在，我们将尝试使用 lmdeploy 进行服务部署。

“模型推理/服务”目前提供两种服务部署方式：TurboMind 和 TritonServer。在这种情况下，服务器可以是 TurboMind 或 TritonServer，而 API 服务器可以提供外部 API 服务。我们推荐使用 TurboMind。

首先，使用以下命令启动服务：

# ApiServer+Turbomind   api_server => AsyncEngine => TurboMind
lmdeploy serve api_server ./workspace \
	--server-name 0.0.0.0 \
	--server-port 23333 \
	--tp 1

在上述参数中，server_name 和 server_port 分别表示服务地址和端口。tp 参数如前所述代表 Tensor 并行性。

之后，用户可以按照TurboMind Service as the Backend 中描述的启动 Web 服务。

网页服务启动方式1:

Gradio 方式启动服务

这一部分主要是将 Gradio 作为前端 Demo 演示。在上一节的基础上，我们不执行后面的 api_client 或 triton_client，而是执行 gradio。 请参考LMDeploy部分获取详细信息。

由于 Gradio 需要本地访问展示界面，因此也需要通过 ssh 将数据转发到本地。命令如下：

ssh -CNg -L 6006:127.0.0.1:6006 [email protected] -p <你的 ssh 端口号>

--TurboMind 服务作为后端

直接启动作为前端的 Gradio。

# Gradio+ApiServer。必须先开启 Server，此时 Gradio 为 Client
lmdeploy serve gradio http://0.0.0.0:23333 --server-port 6006

--其他方式(推荐!!!)

当然，Gradio 也可以直接和 TurboMind 连接，如下所示。

# Gradio+Turbomind(local)
lmdeploy serve gradio ./workspace

可以直接启动 Gradio，此时没有 API Server，TurboMind 直接与 Gradio 通信。

网页服务启动方式2:

Streamlit 方式启动服务：

下载 GouMang 项目模型（如果喜欢请给个 Star）

git clone https://github.com/AI4Bread/GouMang.git
cd GouMang

将 web_demo.py 中的模型路径替换为下载的 GouMang 参数存储路径

在目录中运行 web_demo.py 文件，并在输入以下命令后，查看本教程 5.2 以配置本地端口，将端口映射到本地。在本地浏览器中输入 http://127.0.0.1:6006。

streamlit run /root/personal_assistant/code/InternLM/web_demo.py --server.address 127.0.0.1 --server.port 6006

注意：只有在浏览器中打开 http://127.0.0.1:6006 页面后，模型才会加载。 模型加载完成后，您就可以开始与 西西（句芒） 进行对话了。

开源许可证

本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（<a href="https://wj.qq.com/s2/14897739/e871/" target="_blank">申请表（中文）</a>）。其他问题与合作请联系 [email protected]。

引用