[docs] LoRA conceptual guide #331
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stevhliu
approved these changes
Apr 18, 2023
| This approach has a number of advantages: | ||
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| * LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters. | ||
| * The original pre-trained weights are kept frozen, which means you can have multiple lightweight and portable LoRA models for various downstream tasks built on top of them. |
pacman100
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Apr 19, 2023
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Thank you @MKhalusova for adding this much-needed concept guide! 😄
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| - `bias`: Specifies if the `bias` parameters should be trained. Can be `'none'`, `'all'` or `'lora_only'`. | ||
| - `modules_to_save`: List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task. | ||
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| ## LoRA examples |
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Adding NLP examples such as RLHF using PEFT+TRL will benefit a lot of users
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@pacman100 I absolutely agree! At the moment, I only added the examples that we already have in the docs, once we have more examples, we can add them here too. Likely in a separate PR though.
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
pacman100
approved these changes
Apr 19, 2023
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New doc for PEFT: a brief introduction to LoRA technique (a TLDR) with links to examples.