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sailab-vienna/privesc-llm-4b
privesc-llm-4b is a text generation model from sailab-vienna. Use it when you need the model to write or continue text. It is set up for peft. The card lists the license as mit.
This model artifact contains the paper LoRA adapters for the PrivEsc-LLM paper.
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Updated Sep 14, 2026
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From the Hugging Face model README
This model artifact contains the paper LoRA adapters for the PrivEsc-LLM paper.
Qwen/Qwen3-4B-Instruct-2507cdbee75f17c01a7cc42f958dc650907174af0554Source repository: https://github.com/sailab-vienna/privesc-llm
sft_adapter/: SFT adapter, phase C lr1p5e-4, rank 8, seed 2026rl_adapter/: final RL adapter, Outcome+Cost, step 300Both subfolders contain only PEFT adapter files. Use the base model tokenizer from Qwen/Qwen3-4B-Instruct-2507.
20260515T110310Z_phase_c_lr1p5e-4_r8_ep10_sd2026Outcome+Cost, step 300from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = "Qwen/Qwen3-4B-Instruct-2507"
base_revision = "cdbee75f17c01a7cc42f958dc650907174af0554"
repo = "sailab-vienna/privesc-llm-4b"
tokenizer = AutoTokenizer.from_pretrained(base, revision=base_revision)
model = AutoModelForCausalLM.from_pretrained(base, revision=base_revision, device_map="auto")
model = PeftModel.from_pretrained(model, repo, subfolder="rl_adapter")
Use subfolder="sft_adapter" to load the SFT warm-start adapter instead.
The artifact evaluation workflow pins the evaluated adapter revision.
These adapters are intended for controlled research on local privilege-escalation benchmarks and should not be used against systems without authorization.