Wenhao Chen
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1 year ago | |
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peft | 1 year ago | |
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README.md | 2 years ago |
README.md
Community Examples
We are thrilled to announce the latest updates to ColossalChat, an open-source solution for cloning ChatGPT with a complete RLHF (Reinforcement Learning with Human Feedback) pipeline.
As Colossal-AI undergoes major updates, we are actively maintaining ColossalChat to stay aligned with the project's progress. With the introduction of Community-driven example, we aim to create a collaborative platform for developers to contribute exotic features built on top of ColossalChat.
Community Example
Community-driven Examples is an initiative that allows users to contribute their own examples to the ColossalChat package, fostering a sense of community and making it easy for others to access and benefit from shared work. The primary goal with community-driven examples is to have a community-maintained collection of diverse and exotic functionalities built on top of the ColossalChat package, which is powered by the Colossal-AI project and its Coati module (ColossalAI Talking Intelligence).
For more information about community pipelines, please have a look at this issue.
Community Examples
Community examples consist of both inference and training examples that have been added by the community. Please have a look at the following table to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out. If a community doesn't work as expected, please open an issue and ping the author on it.
Example | Description | Code Example | Colab | Author |
---|---|---|---|---|
Peft | Adding Peft support for SFT and Prompts model training | Huggingface Peft | - | YY Lin |
Train prompts on Ray | A Ray based implementation of Train prompts example | Training On Ray | - | MisterLin1995 |
... | ... | ... | ... | ... |
How to get involved
To join our community-driven initiative, please visit the ColossalChat GitHub repository, review the provided information, and explore the codebase. To contribute, create a new issue outlining your proposed feature or enhancement, and our team will review and provide feedback. We look forward to collaborating with you on this exciting project!