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</li>
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</li>
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<li><a href="#Use-Docker">Use Docker</a></li>
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<li><a href="#Use-Docker">Use Docker</a></li>
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<li><a href="#Community">Community</a></li>
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<li><a href="#Community">Community</a></li>
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<li><a href="#contributing">Contributing</a></li>
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<li><a href="#Contributing">Contributing</a></li>
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<li><a href="#Cite-Us">Cite Us</a></li>
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<li><a href="#Cite-Us">Cite Us</a></li>
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</ul>
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</ul>
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@ -375,7 +375,7 @@ Join the Colossal-AI community on [Forum](https://github.com/hpcaitech/ColossalA
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[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
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[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
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and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your suggestions, feedback, and questions with our engineering team.
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and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your suggestions, feedback, and questions with our engineering team.
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## Invitation to open-source contribution
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## Contributing
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Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!
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Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!
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You may contact us or participate in the following ways:
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You may contact us or participate in the following ways:
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@ -16,7 +16,7 @@ torchrun --standalone --nproc_per_node=2 train_reward_model.py --pretrain "faceb
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```
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```
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### Features and tricks in RM training
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### Features and tricks in RM training
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- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf)and[rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets.
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- We support [Anthropic/hh-rlhf](https://huggingface.co/datasets/Anthropic/hh-rlhf) and [rm-static](https://huggingface.co/datasets/Dahoas/rm-static) datasets.
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- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic).
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- We support 2 kinds of loss_function named 'log_sig'(used by OpenAI) and 'log_exp'(used by Anthropic).
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- We change the loss to valid_acc and pair_dist to monitor progress during training.
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- We change the loss to valid_acc and pair_dist to monitor progress during training.
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- We add special token to the end of the sequence to get better result.
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- We add special token to the end of the sequence to get better result.
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