GLM-130B | GLM-130B: An Open Bilingual Pre-Trained Model (ICLR 2023)

by THUDM Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | GLM-130B Summary

GLM-130B is a Python library. GLM-130B has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

Blog • Download Model • 🪧 Demo • ️ Email • Paper [ICLR 2023]. Google Group (Updates) or Wechat Group or Slack channel (Discussions).

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Quality

Security

License

Reuse

Support

GLM-130B has a medium active ecosystem.

It has 6264 star(s) with 478 fork(s). There are 83 watchers for this library.

It had no major release in the last 6 months.

There are 87 open issues and 76 have been closed. On average issues are closed in 3 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of GLM-130B is current.

Quality

GLM-130B has no bugs reported.

Security

GLM-130B has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

GLM-130B is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

GLM-130B releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

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GLM-130B Key Features

No Key Features are available at this moment for GLM-130B.

GLM-130B Examples and Code Snippets

No Code Snippets are available at this moment for GLM-130B.

Community Discussions

No Community Discussions are available at this moment for GLM-130B.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install GLM-130B

8 * RTX 3090. 4 * RTX 3090. 8 * RTX 2080 Ti. It is recommended to use the an A100 (40G * 8) server, as all GLM-130B evaluation results (~30 tasks) reported can be easily reproduced with a single A100 server in about half a day. With INT8/INT4 quantization, efficient inference on a single server with 4 * RTX 3090 (24G) is possible, see Quantization of GLM-130B for details. Combining quantization and weight offloading techniques, GLM-130B can also be inferenced on servers with even smaller GPU memory, see Low-Resource Inference for details.
Python 3.9+ / CUDA 11+ / PyTorch 1.10+ / DeepSpeed 0.6+ / Apex (installation with CUDA and C++ extensions is required, see here)
SwissArmyTransformer>=0.2.11 is required for quantization

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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