| ---
|
| model_name: VNU-SecAlign
|
| language:
|
| - en
|
| tags:
|
| - security
|
| - instruction-following
|
| - finetuning
|
| license: apache-2.0
|
| base_model: meta-llama/Llama-3.1-8B-Instruct
|
| metrics:
|
| - name: asr
|
| type: error_rate
|
| description: Attack success rate / ASR
|
| datasets:
|
| - Jason-42195/VNU-SecAlign
|
| ---
|
|
|
| VNU-SecAlign: LoRA adapter and datasets for SecAlign experiments.
|
|
|
| This repository contains:
|
| - checkpoints/final_checkpoint: LoRA adapter and tokenizer files.
|
| - data/: datasets used for evaluation and training (moved here).
|
|
|
| Usage (load adapter with PEFT):
|
|
|
| ```python
|
| import torch
|
| from peft import PeftModel
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| # Config
|
| base_model_id = "meta-llama/Llama-3.1-8B-Instruct"
|
| repo_id = "Jason-42195/VNU-SecAlign"
|
| adapter_subfolder = "checkpoints/final_checkpoint"
|
|
|
| # Initialize tokenizer
|
| tokenizer = AutoTokenizer.from_pretrained(base_model_id)
|
|
|
| # Load original model
|
| model = AutoModelForCausalLM.from_pretrained(
|
| base_model_id,
|
| device_map="auto",
|
| trust_remote_code=True
|
| )
|
|
|
| # Load Adapter from subfolder
|
| model = PeftModel.from_pretrained(
|
| model,
|
| repo_id,
|
| subfolder=adapter_subfolder
|
| )
|
|
|
| model.eval()
|
| ```
|
|
|
| Judge used in evaluation: GPT-4o (deployment `gpt-4o`, temperature=0.0).
|
|
|
| |
