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README.md
⚠️ This content may be outdated since the major update of Colossal Chat. We will update this content soon.
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!