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Release update #603
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Release update #603
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…ding single samples
…erecen + enable use of lora adapter
…oader example This notebook explores the customization of Llama Guard 3 for specific application needs. Llama Guard, a versatile AI safety tool, can be adapted to maximise its relevance in various scenarios. We start with zero-shot prompting, a powerful method that allows Llama Guard to make predictions without prior explicit examples. This technique is particularly useful for initial explorations and quick setups. As we progress, we'll delve into adding and removing safety categories before touching on evaluation and fine-tuning processes, where we adjust Llama Guard's parameters to better align with our specific data and use cases. By following the steps in this notebook, the reader should gain a solid understanding of how to tailor Llama Guard 3 effectively, ensuring it performs optimally for your unique requirements. The notebook does not cover every aspect of Llama Guard 3, but focusses on vaious aspects of customization.
Co-authored-by: Suraj Subramanian <5676233+subramen@users.noreply.github.com>
…tuning.ipynb based on feedback in PR
…d_fine_tuning.ipynb
Introduce Llama guard customization notebook and dataset loader
Updates HF model-id from v3 to v3.1
Update README to reflect the new special tokens
Updates to responsible_ai folder including README changes
Inference/Finetuning changes
Adding license and use policy links to llama recipes
Update README.md
Changing top of readme to point to the new repos
Update README.md pointers to new repo
Bumping transformers library version
Update RAI readme
Upstream merge into alpha main
* Add example upgrade script * Modify modelUpgradeExample.py as per suggestions * Reference files in README --------- Co-authored-by: Thomas Robinson <trobinson@meta.com>
Wording on the latest version
subramen
approved these changes
Jul 23, 2024
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LGTM!!
fixing misspelled words
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Release update