https://github.com/tensorchord/modelz-ChatGLM

Deploy ChatGLM on Modelz
https://github.com/tensorchord/modelz-ChatGLM

Last synced: about 2 months ago
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Deploy ChatGLM on Modelz

• Host: GitHub
• URL: https://github.com/tensorchord/modelz-ChatGLM
• Owner: tensorchord
• Created: 2023-03-17T03:46:11.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-03-20T04:36:22.000Z (over 1 year ago)
• Last Synced: 2024-07-31T23:46:17.956Z (4 months ago)
• Language: Dockerfile
• Size: 8.79 KB
• Stars: 15
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Modelz ChatGLM

[ChatGLM](https://github.com/THUDM/ChatGLM-6B) is a large-scale pre-trained language model for open-domain chatbot, which is open-sourced by Tsinghua University.

This repository contains the code (Dockerfile, [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html)) for deploying ChatGLM on [Modelz](https://docs.modelz.ai/).

## Getting Started

This is a template for creating a [Gradio](https://gradio.app/) app on [Modelz](https://modelz.ai/).

Building an Gradio app could be straightforward. You will need to provide three key components:

- A `main.py` file: This file contains the code for making predictions.

- A `requirements.txt` file: This file lists all the dependencies required for the server code to run.

- A `Dockerfile` or a simpler [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html): This file contains instructions for building a Docker image that encapsulates the server code and its dependencies.

## Build

In the `Dockerfile`, you need to define the instructions for building a Docker image that encapsulates the server code and its dependencies.

In most cases, you could use the template in the repository.

```bash

docker build -t docker.io/USER/IMAGE .

docker push docker.io/USER/IMAGE

```

On the other hand, a [`build.envd`](https://envd.tensorchord.ai/guide/getting-started.html) is a simplified alternative to a Dockerfile. It provides python-based interfaces that contains configuration settings for building a image. 

It is easier to use than a Dockerfile as it involves specifying only the dependencies of your machine learning model, not the instructions for CUDA, conda, and other system-level dependencies.

```bash

envd build --output type=image,name=docker.io/USER/IMAGE,push=true

```

## Deploy

Please refer to the [Modelz documentation](https://docs.modelz.ai/gettingstarted/deploy) for more details.