Instructions to use ContinuousAT/Phi-CAPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ContinuousAT/Phi-CAPO with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct") model = PeftModel.from_pretrained(base_model, "ContinuousAT/Phi-CAPO") - Notebooks
- Google Colab
- Kaggle
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# Model Card for Model ID
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In this repo are LoRa weights of the Phi-3-mini-4k-instruct model (https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) finetuned with the Continuous Adversarial
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For more information, see our paper "Efficient Adversarial Training in LLMs with Continuous Attacks" (https://arxiv.org/abs/2405.15589)
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## Github
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# Model Card for Model ID
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In this repo are LoRa weights of the Phi-3-mini-4k-instruct model (https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) finetuned with the Continuous Adversarial Preference Optimisation (CAPO) algorithm.
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For more information, see our paper "Efficient Adversarial Training in LLMs with Continuous Attacks" (https://arxiv.org/abs/2405.15589)
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## Github
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