Instructions to use INSAIT-Institute/Qwen-14B-MixAT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use INSAIT-Institute/Qwen-14B-MixAT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct") model = PeftModel.from_pretrained(base_model, "INSAIT-Institute/Qwen-14B-MixAT") - Notebooks
- Google Colab
- Kaggle
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This is a model adapter for [Qwen/Qwen2.5-14B-Instruct](Qwen/Qwen2.5-14B-Instruct), fine-tuned using the MixAT method. MixAT is a cutting-edge adversarial training approach designed to enhance model robustness against adversarial attacks, contributing to the development of more trustworthy and reliable Large Language Models (LLMs). For details, see our paper [MixAT: Combining Continuous and Discrete Adversarial Training for LLMs](https://arxiv.org/abs/2505.16947). Training and evaluation code is available in the [MixAT Github repository](https://github.com/insait-institute/MixAT).
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## Use in 🤗 PEFT and Transformers (Quantized)
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This is a model adapter for [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct), fine-tuned using the MixAT method. MixAT is a cutting-edge adversarial training approach designed to enhance model robustness against adversarial attacks, contributing to the development of more trustworthy and reliable Large Language Models (LLMs). For details, see our paper [MixAT: Combining Continuous and Discrete Adversarial Training for LLMs](https://arxiv.org/abs/2505.16947). Training and evaluation code is available in the [MixAT Github repository](https://github.com/insait-institute/MixAT).
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## Use in 🤗 PEFT and Transformers (Quantized)
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