6cd83b6b0e
The English version README files of the following documents are created and translated. 1. demo/README.md 2. evaluate/README.md 3. xtuner_config/README.md 4. xtuner_config/images/README.md 5. xtuner_config/ChatGLM3-6b-ft.md There are some format problem and language expression in the Chinese version, I also adapted them. By the way, I modified the file name of `evaluate/General evaluation.md` and `evaluate/Professional evaluation.md` since they are shown in the `xtuner_config/README.md`
59 lines
1.1 KiB
Markdown
59 lines
1.1 KiB
Markdown
# Deploying Guide for EmoLLM
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## Local Deployment
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- Clone repo
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```bash
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git clone https://github.com/aJupyter/EmoLLM.git
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```
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- Install dependencies
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```bash
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pip install -r requirements.txt
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```
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- Download the model
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- Model weights:https://openxlab.org.cn/models/detail/jujimeizuo/EmoLLM_Model
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- Download via openxlab.model.download, see [cli_internlm2](./cli_internlm2.py) for details
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```bash
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from openxlab.model import download
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download(model_repo='jujimeizuo/EmoLLM_Model', output='model')
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```
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- You can also download manually and place it in the `./model` directory, then delete the above code.
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- cli_demo
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```bash
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python ./demo/cli_internlm2.py
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```
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- web_demo
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```bash
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python ./app.py
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```
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If deploying on a server, you need to configure local port mapping.
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## Deploy on OpenXLab
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- Log in to OpenXLab and create a Gradio application
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![Login OpenXLab](../assets/deploy_1.png)
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- Select configurations and create the project
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![config](../assets/deploy_2.png)
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- Wait for the build and startup
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![wait a minutes](../assets/deploy_3.png)
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- Try your own project
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![enjoy](../assets/deploy_4.png) |