Instructions to use sailab-vienna/privesc-llm-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sailab-vienna/privesc-llm-4b with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
PrivEsc-LLM 4B Adapters
This model artifact contains the paper LoRA adapters for the PrivEsc-LLM paper.
Base model: Qwen/Qwen3-4B-Instruct-2507
Source repository: https://github.com/sailab-vienna/privesc-llm
Layout
sft_adapter/: SFT adapter, phase Clr1p5e-4, rank 8, seed 2026rl_adapter/: final RL adapter,Outcome+Cost, step 300
Both subfolders contain only PEFT adapter files. Use the base model tokenizer from Qwen/Qwen3-4B-Instruct-2507.
Provenance
- SFT selection run:
20260515T110310Z_phase_c_lr1p5e-4_r8_ep10_sd2026 - RL selection:
Outcome+Cost, step 300
Loading
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Qwen/Qwen3-4B-Instruct-2507"
repo = "sailab-vienna/privesc-llm-4b"
tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, repo, subfolder="rl_adapter")
Use subfolder="sft_adapter" to load the SFT warm-start adapter instead.
These adapters are intended for controlled research on local privilege-escalation benchmarks and should not be used against systems without authorization.
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Base model
Qwen/Qwen3-4B-Instruct-2507